{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.2 Plotting with pandas and seaborn（用pandas和seaborn绘图）\n",
    "\n",
    "matplotlib是一个相对底层的工具。pandas自身有内建的可视化工具。另一个库seaborn则是用来做一些统计图形。\n",
    "\n",
    "> 导入seaborn会改变matlotlib默认的颜色和绘图样式，提高可读性和美感。即使不适用seaborn的API，也可以利用seaborn来提高可视化的效果。\n",
    "\n",
    "# 1 Line Plots（线图）\n",
    "\n",
    "Series和DataFrame各自都有plot属性，用来做一些比较基本的绘图类型。默认，plot()会绘制线图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# 如果不添加这句，是无法直接在jupyter里看到图的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e7f4a8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K1Bbvvw8HHZQC3ogR2Z4rk3AQQtguhDA7hDCr6WvzrTaLc0qSWrb00nDvvelK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\nMBxIkqQShgNJklTCcCBJkkr8f3brlNjvN+qxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ebab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series对象的index（索引），被matplotlib用来当做x轴，当然，我们也可以自己设定不这么做，use_index=False。x轴的ticks（标记）和limits（范围）能通过xticks和xlim选项来设定，而y轴的可以用yticks和ylim来设定。下面是关于plot的一些选项。\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/ebgnj.png)\n",
    "\n",
    "大部分的pandas绘图方法接受一个ax参数，可以作为一个matplotlib subplot对象。这给我们更强的灵活性在gird layout（网格样式）中放置subplot。\n",
    "\n",
    "DataFrame的plot方法，会把每一列画出一条线，所有的线会画在同一个subplot（子图）上，而且可以添加legend（图例）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "                  columns=['A', 'B', 'C', 'D'],\n",
    "                  index=np.arange(0, 100, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113025b38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A3Ji53Db9NoKqB7H50c20qNXiur7f0cGRT+/9lK97fM3krZMJnxVOel66laIV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\nIiIiEuX/A6jNJE6g1r9EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113117dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot属性中，包含一组方法，来画出不同类型的绘图。例如，df.plot()等同于df.plot.line()。\n",
    "\n",
    "> 一些额外的关键字可以在plot里设定，并会被matplotlib函数执行，所以我们可以学习matplotlib API来定制化想要的绘图。\n",
    "\n",
    "DataFrame有一些选项在处理列的时候提供了灵活性；例如，是否把所有列都画在一个子图中，或者把不同列画在不同的子图中。下图有更多的设定：\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/tma2m.png)\n",
    "\n",
    "# 2 Bar Plots（条形图）\n",
    "\n",
    "plot.bar()和plot.barh()分别绘制垂直和水平的条形图。这种情况下，series或DataFrame的index会被用来作为x(bar)或y(barh)的ticks（标记）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146a6748>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBQvaMCNVzWJBklTX6OgoS5YsYWxsrOG+8+bNY8WKFRYMfcBiQZJU1/j4OGNjY8yaNYvZ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114362978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "data.plot.bar(ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot.barh(ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "color='k'设置颜色为黑，而alpha=0.7则设置局部透明度（靠近1越明显，靠近0则虚化）。\n",
    "\n",
    "对于DataFrame，条形图绘图会把每一行作为一个组画出来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.rand(6, 4),\n",
    "                  index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
    "                  columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Genus</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.857118</td>\n",
       "      <td>0.055367</td>\n",
       "      <td>0.232816</td>\n",
       "      <td>0.007077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.554345</td>\n",
       "      <td>0.491691</td>\n",
       "      <td>0.515788</td>\n",
       "      <td>0.217826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.843414</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.070781</td>\n",
       "      <td>0.074844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>0.180180</td>\n",
       "      <td>0.976074</td>\n",
       "      <td>0.135649</td>\n",
       "      <td>0.471574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>0.670680</td>\n",
       "      <td>0.122028</td>\n",
       "      <td>0.307744</td>\n",
       "      <td>0.440098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>0.546968</td>\n",
       "      <td>0.800105</td>\n",
       "      <td>0.421170</td>\n",
       "      <td>0.821478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Genus         A         B         C         D\n",
       "one    0.857118  0.055367  0.232816  0.007077\n",
       "two    0.554345  0.491691  0.515788  0.217826\n",
       "three  0.843414  0.240387  0.070781  0.074844\n",
       "four   0.180180  0.976074  0.135649  0.471574\n",
       "five   0.670680  0.122028  0.307744  0.440098\n",
       "six    0.546968  0.800105  0.421170  0.821478"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146f67f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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33HMAPPXUUxlXIklqZgaAJvLcc89x/fXXM2rUKLq6urIuR5LUxFr3InKWWwEMwblTSlx3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\nkiTl0P8HVCTOxEt0/98AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148c22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1173d7860>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4AksHAN+5IjRUbcOr/kHtT376KVtjxtynsLAwPfzwWF15ZZzKy8u1Zcv/04IFc/T22yus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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174600f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar() # 可以看到导入seaborn后画出来的图漂亮多了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意这里DataFrame列名的\"Genus\"，被作为图例。\n",
    "\n",
    "我们可以设定stacked=True，令条形图堆叠起来，能让每一行的所有值都被堆起来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11755bb38>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vSZMmSZJuvvlmrVy5Uk8//bSmT5/e7m3HYjHNnz9fubm5narNtvcN0m6xbUu2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\nAADGIeQCAADAOIRcAAAAGOf/B2WibcLApJZlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175ba8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.barh(stacked=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 一个小窍门，在画series的值出现的频率的条形图时，可以使用value_counts: `s.value_counts().plot.bar()`\n",
    "\n",
    "之前我们用到过tipping(小费)数据集，假设我们想做一个堆叠的条形图，来表示在每一天，每一个大小不同的组（party）中，数据点的百分比。用read_csv导入数据，并按天数（day）和组大小（party size）做一个交叉报表（cross-tabulation）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips = pd.read_csv('../examples/tips.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size\n",
       "0       16.99  1.01     No  Sun  Dinner     2\n",
       "1       10.34  1.66     No  Sun  Dinner     3\n",
       "2       21.01  3.50     No  Sun  Dinner     3\n",
       "3       23.68  3.31     No  Sun  Dinner     2\n",
       "4       24.59  3.61     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "party_counts = pd.crosstab(tips['day'], tips['size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size  1   2   3   4  5  6\n",
       "day                      \n",
       "Fri   1  16   1   1  0  0\n",
       "Sat   2  53  18  13  1  0\n",
       "Sun   0  39  15  18  3  1\n",
       "Thur  1  48   4   5  1  3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size   2   3   4  5\n",
       "day                \n",
       "Fri   16   1   1  0\n",
       "Sat   53  18  13  1\n",
       "Sun   39  15  18  3\n",
       "Thur  48   4   5  1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大于1人小于6人\n",
    "party_counts = party_counts.loc[:, 2:5]\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标准化一下，让每一行的和变为1，然后绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>0.011765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.017241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size         2         3         4         5\n",
       "day                                         \n",
       "Fri   0.888889  0.055556  0.055556  0.000000\n",
       "Sat   0.623529  0.211765  0.152941  0.011765\n",
       "Sun   0.520000  0.200000  0.240000  0.040000\n",
       "Thur  0.827586  0.068966  0.086207  0.017241"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize to sum to 1\n",
    "party_pcts = party_counts.div(party_counts.sum(1), axis=0)\n",
    "party_pcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11807af28>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hyMhIJSQM1tKlz2jkyPOsLrEJ4RMAAMAHkpNT9YjmmOns3BP9eWvmzAd9UMwPI3wCAAD4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\ngDGETwAAABjz/wAWrGVTvVs7aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175eaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_pcts.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样我们可以看出来，在周末的时候组大小（party size）是增大的。\n",
    "\n",
    "对于需要汇总的数据，使用seaborn能方便很多。让我们试一下用seaborn，按day来查看tipping percentage(小费百分比)："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.063204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.172069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.063204\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.191244\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.199886\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.162494\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.172069"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1182b3cf8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I5XLxox/9KP74xz/G008/HX379o1LL700n7MCAEBBNOunZ4wdOzbeeuut2HvvveOSSy6J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11806bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='tip_pct', y='day', data=tips, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seaborn的绘图函数中有一个data参数，这里可以导入pandas的DataFrame。其他参数指的是列名。因为每一天（比如一个固定的周六）可能会有多个不同的值，所以条形图表示的是tip_pct的平均值。条形图上的黑线表示95%的置信区间（confidence interval）（这个可以通过可选参数进行更改）。\n",
    "\n",
    "seaborn.barplot有一个hue选项，这个能让我们通过一个额外的类别值把数据分开："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"ticks\") # 我们可以换一个样式\n",
    "# sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e143c8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAYQrAEAAAADCNYAAACAAQRrAAAAwACCNQAAAGAAwRoAAAAwgGANAAAAGECwBgAAAAwg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11825f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意seaborn会自动更改绘图的外观：默认的调色板，绘图背景，网格颜色。我们可以自己设定不同的绘图外观，通过seaborn.set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Histograms and Density Plots（柱状图和密度图）\n",
    "\n",
    "柱状图是一种条形图，不过值的频率是分割式的。数据点被分割为，离散的甚至是隔开的bin（BIN是储存箱、存放箱、垃圾箱的意思，中文实在是不好翻译，我把它理解为一个小柱子，之后就直接用bin了），而且每个bin中的数据点的数量会被画出来。用上面的tipping数据集，我们可以用plot.hist做一个柱状图来表示小费（tip）占总费用（total bill）的比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1191d05f8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aHnZnzpzZ998TJ07UpZdeqoKCAr311ls6//zzQ3ott9vdt2pshaampiG5RlJSUtivEw2a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\npYQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187c5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种相关的绘图类型是density plot（密度图），这个是用来计算观测数据中，连续概率分布的推测值。通常的步骤是用一组混合的“kernels”（核）来近似这个分布————核指的是，像正态分布一样的简单分布。因此，概率图也经常被叫做kernel density estimate(KDE, 核密度估计)图。用plot.kde，通过conventional mixture-of-normals estimate（常规混合估计\n",
    "）制作一个密度图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11945a748>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AcIKwCmBK5slVEp1VAMD0IqwCmJJ13rTQzmp1gLAKAHCOsApgStaP8B13VhkDAAA4QFgF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119462f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'].plot.density()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seaborn能更方便地绘制柱状图和概率图，通过distplot方法，这个方法可以同时绘制一个柱状图和a continuous density estimate（一个连续密度估计）。举个例子，考虑一个bimodal distribution（双峰分布，二项分布），它由连个不同的标准正态分布组成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp1 = np.random.normal(0, 1, size=200)\n",
    "comp2 = np.random.normal(10, 2, size=200)\n",
    "values = pd.Series(np.concatenate([comp1, comp2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.006016\n",
       "1   -0.481471\n",
       "2   -0.861770\n",
       "3   -0.544488\n",
       "4    0.712472\n",
       "5    0.371618\n",
       "6   -1.289412\n",
       "7    1.201699\n",
       "8    1.433649\n",
       "9   -0.076823\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114fc67f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8eMR9HWs/RxIRwV/7cKhtV9o3NDabRVarZLVaJElWa2CezWaV01mqWbNeUkyMQ5JUWlqq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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fb4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(values, bins=100, color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（我在运行上面的代码时，报错提示为ypeError: slice indices must be integers or None or have an __index__ method。通过更新statsmodels这个包解决了问题，原先的版本是0.6，更新到0.8后就没问题了。可以直接输入`conda install -c statsmodels statsmodels=0.8.0\n",
    "`）\n",
    "\n",
    "# 4 Scatter or Point Plots（散点图或点图）\n",
    "散点图对于检查二维数据之间的关系是非常有用的。例如，我们导入macrodata数据集，选一些参数，然后计算log differences（对数差分）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('../examples/macrodata.csv')\n",
    "macro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cpi     m1  tbilrate  unemp\n",
       "0  28.98  139.7      2.82    5.8\n",
       "1  29.15  141.7      3.08    5.1\n",
       "2  29.35  140.5      3.82    5.3\n",
       "3  29.37  140.0      4.33    5.6\n",
       "4  29.54  139.6      3.50    5.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.007904</td>\n",
       "      <td>0.045361</td>\n",
       "      <td>-0.396881</td>\n",
       "      <td>0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.021979</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>-2.277267</td>\n",
       "      <td>0.139762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.010286</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.160343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>0.127339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0.042560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cpi        m1  tbilrate     unemp\n",
       "198 -0.007904  0.045361 -0.396881  0.105361\n",
       "199 -0.021979  0.066753 -2.277267  0.139762\n",
       "200  0.002340  0.010286  0.606136  0.160343\n",
       "201  0.008419  0.037461 -0.200671  0.127339\n",
       "202  0.008894  0.012202 -0.405465  0.042560"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_data = np.log(data).diff().dropna()\n",
    "trans_data[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于对数差分译者找到的一些资料：\n",
    "\n",
    "1 \n",
    "1. 取对数(log)：缩小差距，减少异方差性\n",
    "2. 差分：非平缓数据变平稳的技能\n",
    "\n",
    "2 是原始序列的对数增长率，而且这么处理后序列会更平稳\n",
    "\n",
    "3 \n",
    "对于不平稳的时间序列，我们可以通过差分的方法使它平稳，但是差分之后的问题是有的经济意义就无法直观解释了，所以我们又有了构建协整关系这一方法。\n",
    "\n",
    "\n",
    "然后我们可以利用seaborn的regplot方法，它可以产生一个散点图并拟合一条回归线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11556f898>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sLMTTTz+NW2+9Fa+++ioKCgoSPo7f74fX603q3JRZ4UGfHPxpTslen/oLS1F/YSkAIKD6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\nfN4ANQnMbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155466d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('m1', 'unemp', data=trans_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在数据探索阶段，散点图能把一组变量之间所有的散点图都画出来，这种图被称之为pairs plot（多变量图）或scatter plot matrix（散点图矩阵）。画这样的图很麻烦，所以seaborn有一个非常方便的pairplot函数，这个函数可以把每一个参数的柱状图或密度估计画在对角线上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x116370390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xg4kPzl955RXyPOf8+fPj1+6++25eeumlLdu+9NJL3H333Rteu+uuu/jxj38M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1JUnKdMWjWvcxdBXL0lHI6tdS56oQQrRasN+Y89zcMede2IoiiS6x1L3vYO+JSZJSc0LqAdSc\nENtOO/q5VawuCc5FRznYrkWdNIbbMrRFl5v64suFWC2N+lWu+ZRqWYtA3tYZ7LNRUJY8V7vthZkQ\novM0gnNNU1BVBV1X0TWVKE6kW7s4LI17X0qK70eEUYKhqwz32y/7bxv31ZmyS7kWMD7jEiaadIkX\nS5LgXHSUA3Ut6i1YwMuP4V7rQOLl9lfMmVStcN5x5W2DvC3VU6ytRv3KWQaWGeEHMY4XUcjFDPTY\nFHPmgn+znBdmlbq/IvVu/zqlpu38+k0IsRYawfncl4h5W6dSX/jcIMRyFHMmFTNg10StOeTQMjWq\n9ZDegnXAe1nNDajPDlV0w2xaP9MK5z23Hip5Md6d5OlfdJSD6VrUuBC6fkgQJaRR1Lww5vNrm9Tj\nYAIXVVUYHS40L7C9OQWvosgFVqy5Rj1SFYWhXpvpqofrh2iKwshgftFz8mBemKXA2LQDitFc51Dr\n3WJ1irS9e8cIIVZf4/pl6vvSKeUsCc7F4VNVhWLewLY0VE3B1FVyloEfxs173VKBsudHTJZcqjWf\nmhMyU/WJUpX+onlYwbkkqeteEpyLjrJUt9q5yxsXwkZymCAIqHkpXhAfVCCxkg52f6qqNH93HAe5\nrIpWaNSjJE2Zqnj4QYyCSpym7J12Fr3pH8wLszBW8PwY294XnB9qvVusTnl+TBhLrRHiSNYMzuc8\nDzQytkuCVXG4wjihYJsU9lvuh/EBA+UoTuclK4QseWEcH94r5bV+nhVrR7K1i45SzJnzpkeBLKBI\nkpSJkkOl7hNEyYILoedHJEly2Ek9lmut9ydEo/t4oz4sZ0q+Rv1y/bBZh/K2jmVqS2amPZgXZuES\nDyGHUg+W+jdL7UMIcWQIZqdSM435Lecg2drF4TvQve5AgbKmKwuGKeZtHVU/+BfKi93X5fmye0nL\nuego+3cBN3WVaj1ksuQ11/HDCNvS8WYvlIoChbxNzQsxrYSUtJl9umGpi+7hOpjARYiVsv/b+5SU\nvalDX9HEtvSXHY/WqF9REpPEKYahYZnaAbO1L5YzimOiGgAAIABJREFUITe7rwZDW3yfh1IPlvo3\nS+1DCHFkaLxQNPcbcw4SnIvDd6B73VTFXZAszrJ0/DAmZ2ZJVXU1oV4xGe636e2x580mcCBLtcoX\ncov/e3m+7HwSnIuOM7cLeKXu481pJU9J8fyIKIop5nUURaFcDXFcn7obEcZ16m5EX9HAtgwUlAWB\nxEo6mMBFiJUy9+19Ssp02cPxIhzfomCbBzUeTVUVBnpswnBfS3SSprh+iGEoVOr+vCB//xdmjbF2\nkNXPStUjBSxrfketQ60Hi9Up29LwNGk5F+JI1ph+dP8x5yDBuTiwg0msttS9TlUVNEVh10SVcj3E\n0BQsQ6eYM1jXn2ves3xPw9BSLEMjbxkHff9bqlW+kNNne7rJ82W3keBcdLRGS14jKJ8sucRxSqqk\nRHGKpmZd3UxTy5LC+Vmg4QUqiqJw9Pqel820uZQkSQlihemKR5Qu/2IuxEqb27Lt+1FznGUQJRRY\nfDzaYufx3AA4SVMmSy6aqhDYCePT7ssG+UmSjVF3/QjPC5gq+6wfURgesAnj5LDqwWJ1Sk0NyhPL\n3pQQoovImHOxHI2A3PMjyrUAFJq9xJa6x81tHJq7ncmSS6kS4AZZ45CiqBw1lCdN0+Y9y1BjpidN\n1g/kGB48+KRtSw/lSuT5sktJcC46mmVozRbCUtVjppqNiT1qMIedU5mY9kiTGFPX8IME28oyUeuq\nim0aqOqhZUVPkpSxaYfJsk9PNcALVapWyMhgnpobUKr5APQXLXoLliTnEGti3jjvKGn+PLclae6N\nfrHzuGwE9BQMVB0UP8XxAixDo6doNB9c5gb5i3W5S9I6iqKQpjSnj5mu+Kwb6mFdf76570OdWm3/\nByTHkTF2QhzpGt3arbljzqVbu1jE3PtW3QuYLvvYlk7OVgmjlLqnUszr9BVffh7zmhtQd0N6CgZ1\nP5sZyDZ1gihiz6RzUNs4kAMNj1zsZYHofBKci46VJClRlDA14zI2VSed/Z+pq0yWPLSqgh/G1F2f\nWjVgRPUxDQM/irFCFcUDvcQhBQfZ29b5AYHjh2zbNcNU2ccPYlJSDE1hsC/HxnXFQ26hPxhRlDA+\n41D3Agq2yfqBPLou+R6PNHNbvI3Z7980VJIkZabmYeoq6wb2PSjsfx4nacqO8So9BQPHzVrewyhB\nU6Hq+uQtnTgBVVPQdYW8ZTA+4zA+XcfQVQxTw/MjpsouPXkTz4uYqbo4fkKp5rNrvE5PPnuQ2DNZ\nx/HD5hi9Yt7kuA19ct4KIQ5J0JxKbWHLuQTnYq65XcWDKCFJU3ZP1ugrmCiKgh9GuF7E5mMHl3x2\na7S8752uEycJu8ar7Bx3Zv/qEycJG9f3UHV8ylWf7btn2DkZgznDsSEcPdJ7UM+Eyxke2SiTG0TE\nUYquKQeVb0a0FwnORdtIkpQwVtg5UUPXA0xDwzBUcua+C8vcbkgzVZ+ZssNE2Wem5mHoGrqmYBoa\npaqHqqgYukpf3qJWSag6ITW3TD5nEEYxjhfT32Nx/GgfmqbO68b0cuOPFutm5PsR01U/61ZPSqUW\n4AUxfpgQxQnDffmX7QpccwMqVY8gVoii5KBaFqMo4TfbJpmp+rNL6oxPO5x6wrAEOkeYuV2+3cBA\n11WmSh6lakCSpmgaqCock0JvwcINIvwwm4bMD2MiAoIgZiaKmaq6hEGM7yeUHR/D0CjMzutazBvY\nukap4hMmMbV61v09nE1+U3dDdu2tAiq6mjBV9unpDYnT7Jx2vIjdU1XqbkgcZ71ZSrWAlJQTjx6Q\nhwghxLI1g/M5Led5K5u+0fUj0jRFUeTaciR4uZ5Zc5/hTF3FDyPCMJvRxwtiwjDB1DV2jFexDZee\ngkEUpwRBhB/F2LqGHyd4QUSlFjJdrlNxAlASDE0nTRLCOMUPIiZKDo8/O8X2XTPMlMvsKcFEOSSf\nMxie7Ul2oOOouQGGrqAoOpquLPpM7IcxhpYlSPbCqJlvxjI1hvtzFGyZ/7yTSHAu2kKSpOyZrvPi\nhE8pquAFKUGYMNSXY/2gzUBPjqOGCs1xrHUvYPdkjXI1IIlTKk5IFHn05k3SNCWMU/KWgm1p5C2F\nas0mSFOiMML1Q6ZKYBgKaZLwXJSwYX2RJM0ucsWcueR8lY0L29xuRtl495BSzSdOsnmhg9kLPGTd\ni4MowfUjKnUfVVUW3DDmdrHyvICJss8zL84w2F982TFQ4zPOnMA8M1P1mZhxGF1XXJXvS7SvRje3\n3oKFpWvNOVbrbkCSwOSMD1QxjTqT0w4v7qlQrYZYU3XCRKHmBISzPTFmqiG6mj30xnHKYL+Nriqo\nSp6JGRdFyR6EK3Uf149w/JCibVD3IqpuQLUWsH4gh6FnrfcpsHO8ShgmlCoBU2UPw1DpL1qoikK5\nHsgcrUKIQ7JvKjUNyBJENrq1J0lKECWSyfoIkAJj0w4oRnPZ/s9PhqZS9wKCKJm9p2XL0zQlChMs\nUyOOE8Ym6pi6RqKmlCoBM1WPJI5JUdBVGOzP0Zc32T3lsmu8hq6p1JKY9YM5+osGrh8zVfLYvrtE\nlKSgqERJyvbdJU7Y2LtocD639btc9YF95ctZOsW+hc+NAJ4fUnFCijmjmWPBD2JcP0RVlCXvrfs3\nDC1n+lWxOiQ4F22h5gZUaiF1NyTXEzNVCfD8BDeISElxvBgUml1wgyjB82PGpuuYukrNCXC9mJoT\ncOxoP4O9BkU7677reB4VNyWIfdwQSmWfnoKOqqqUqyFuX0Tdjxjqs+nLG1lyj7JDECWYukrOMhYk\n0irmTGxLQ1EUStWAKMneyrt+RJyk6HMCaENXMXWVlJSd41Vsc+ENY/9snFGcbbeQj7HNfd3yFru4\n1r2Fc08D1JZYLrpf42Y7UXIgTdF0iGeHoCvA1LRLyfEp1wM8L2SqEuDHFVJVhSQ7j8tOiOf7+GHK\nQNEkCBPiJKWYM3HDiHLNz4aIGBp7px1KdR9T13BcnyhWKNc8QGHPZI2hHotiQcdxQnK2jqGrRLMF\nylorInKmMduCIePHhRDL54fZPTRrOc+uI41u7QCOF0pw3kEOJoP6YsJYwfNjbHvfs9b+eVJqTojn\nx80g1tBVRtflUUiJ4wQ/iJn2QuI0JWeoRGmKG8TsmawThjE1NzuXBqoBxx9VJE4SFFXFMjWSJKVU\n9egrmgwULabKDnUvZrJUx/N8Ki4M9xcoVd1Fj7nZUOOHTJS8Zuu3qijzjqPx3NiYwq1U86m7IUma\nzNtmIyHsYvfW/RuGJss+Y9MOx+fz0sreQh0fnAdBwGc/+1n+5V/+Bdu2+bM/+zMuu+yyRdd9+umn\n+exnP8uzzz7Lq171Kj772c9yyimnrHGJxWL8MG4msPL9BM/Pfo6SlDBKcGbHruZMkyRNCcKY6bJH\nzQlBSak7IW4Qk7cMqm5Ab9HADUM8N2a66rBzos7IUA/VmkOcQs0JyVk6paqPbet4YYzrRwz32YRx\nytikg6Iwe0MIyOcs9k5nZWrcII4azLO7YKGqMNhjYRs6kyWXsRkHQ8u61eVtjYKtk7MMfD8iTue/\nkWxcaPe/aEazby7DcF9w3vic9lewTaC+YHnRluk0jkRBELP1xSlmaj4KEMcpUZJiqCopWYbXKEqo\nOxFRnFKwdWqWhR8mDPbbOK6PH0YEfkwYASm4foxpalTrIZqioBsqQ70xUyUX09Co1ANMVSXwI0g1\ntu0qkSQpqqKwbiBHqerh+zHWYFYXUFJsS2W6nKBpClGSYpkaOcuQh2chxCHxGy3nukYjOM/PCc5d\nP2KgpxUlE8u11NzeB9M1O4wXb/ltPD/V3GzI4WCfTSEXE4YxuqFmvcBqAbt9F1UBL4wpzd5HNRW8\nIMb1QoIoye6tCfhexEsTdaZmXKIoZsrLeo/lbR3bUDFNFU1VKNdcNE1F03Q0Tc1+NxYOO5zbUNN8\nJp5t/S7MPtM1jsMP43lTpkZxwkw1IElSTFNvtrY3EsIudm9dbJo2z4+lB1uLdXxw/uUvf5mnn36a\nu+66i507d3LdddexceNGLrzwwnnrua7LFVdcwbvf/W5uuOEGvvWtb/GRj3yEn/70p9j24WVSFIfP\nMrRmAquUfRdWXVWarWx+EBNF/myg7gFZkG4aWTchQ1ezLK1pwviMy1CPQaKolGsBYRize6JOMW9S\n91wUXcU2NWpuwEzFp7dgYUcxz2yfxvMjal6MgkJPXsc2NQoFn6PX9SwyjVRCX8HCnr1orhvMU8hn\n1Wr9YIqiZlNzlOs+KiyawKPxVhiyhFzZRVYhiLIbxv6f0/7WD+QZn57ftX2gx2LdwIHHMonuE0UJ\nj/52nO17KkB2PhmqQqGoo6KTpBDFMXGaYugKUaKQpimBH6GaBrqqUCxYlGsBpqFgWSauF7F+MMcL\nuyvkLQPXC+k1LKbLDjlLp+r4zfrZX7R5cW+VIMjqpW1qhGHEwEAOXdPI5fZ1gU9TyFkqjhsT+hGG\nrmDPJq+bKDkyLYwQYlkaY84Xy9YO4Mp0ah1jqbm9D6ZrNqjzniMbGs9PbpC1SodRgqGrFAtm9rxX\nNDA0lbEph8APqdYDXC8kZ+rU/GxomOtlAbNuKBgmFHMG5Xp2D9RUBdPWiZIYRc1mA9oz7uCHET15\nkxd2VwmjCMdPOG5DD6a6MDifW7YkTVHIuuk3Wr/nHodlaPOmTDU0lbytgQqaBmlC86X3Ugnkluqp\nJj3YWqujg3PXdbnnnnu4/fbb2bx5M5s3b+byyy/n7rvvXhCc/+AHPyCXy/GJT3wCgOuvv56f//zn\n/PjHP+Y973lPK4ovmD/PpGmoFPNZd3HLiAijlJypUan7pCkMD1iUaj6T0y4Vx8dxA4Z6bcamapRr\nPjlLpxynVOsBxbxJtW4QRwlpmmBZOkmq4gYhBdtAUUBVIG8bDPZZGJpGnMBk2cPzYgxdQdPULOBV\n4Ri9iG3M716uK2Bo+82BqSgUcxbrB3PkLYPtu8tUnYCcqZOmKVMVr9k9qaERhJSNgB3jVao1H9cP\nKKbguCG2qaOgkLN08paxIMmJrqucesIwEzMONS+gaJusk2ztR5RGPdo5XmHH3gp+GKGrKrqmEiYp\nPbaB62c3+zBOmJj2MI0sKVzd8dF1BU1RME2Net3H8WPqXsRgj0Vq6+wYqzDQY9Gb09ENnZmKh6Hl\nmarUyFsWhp6iqTpBGDPUY+H6IbahgQIVN0JXUvYWHRIUSKHmRfQWdIIopewE+JGGtrdKtR6ycaSI\npmTnbmNaNz+KJfOsEGJJ8WwvO1h8nnOQjO2dZDlB4/5ds0s1D6sYYdtpM2dPIzhNkpRy1We85BKG\nMVGU0Fs0GRkuYOkalRgKts74tMOeyTpBFBNECcN9NqahEqcpnp/NLmJqCnESEYUxmqpRcTxSoOZm\nrecvjVUIo4SCrRFFMa8Y7cHzA2zLJAhjgjiZdwyVus+OsSrjMw6WoaMo2Qt3U9eard9zg+zsPrjv\nOS8F1vXnKORNbEulx7ZQlawnZhBFvLS30pzed7H8SXNJD7bW6ujgfOvWrcRxzBlnnNFctmXLFm69\n9dYF6z7xxBNs2bJl3rKzzjqLxx57TILzVbbUuKH9uy0laUpvwWRoXQ5QqbsRcZpQr/j0FUz2Ttbx\nw4RyzcXxYiYrHioKqqpi6RqkoJAlh7MtjVItwfFidA0CPyElRVEVhnpz9ORUHD+hN2di6gp1L2aw\nqBGGKZCi6zpxkhAnCYaqoaAwVfEY6rcJg5i90wl9OQ1DS7Gt+RexxsWz5gYkZNNOuUE0m21TwfXD\n2XHsIZqqkCRZz42egkFvwUQlxu+zeeVoERQd01AZ6LHJW0YzIV5DoxVf11VJ/tblXq4eOX7I87uq\njE05+EFMztaxLY2ibeKHCcP9OaYrHntnHJwgYHwm4qjBBMvSGO636e/vJQpBMzQ2DOdJ4hy6Dral\nsnfagBQKOY3dEy5uGDNd8YjjhCD0GOyxqLsRhYJJFMdsHCpQcUNKFT/LwdBvUql55CyTnoKBbag4\ns0njBnttFEUhBsZnsqy4Az25Rad1W4nMs4c6jlEI0b7Cudm352VrnzPmXILzjrGcoHH/VnaF2ecw\n20A31HnX+UrdJ02yXhRVJyRJUyYrHlGckDN1ElISUup+iBdmyeKK+Ww45dHDeXKWgRckpGlE3QuJ\nE41dkw6WodJXsHCCkL6CSYrCZMkjiBKOGy2i6xqTZY8wjHD9hOFem3R2bHiSpOyeqLF3us7YZH22\n9Tyir2ij6yrD/TbrB/OLvpjuLZpMll0UsvNe1zRUYH1/gd6Cxa7xGjvGq/izCYrzts7GdUU2rCui\nqsqi07TZlrZoK7tYOx0dnE9MTNDf34+u7zuMoaEhfN9nZmaGgYGB5vLx8XE2bdo0798PDQ3x3HPP\nrVl5j0QHGjc094KaklKthYxPe3ipy0wlyxgdxhHlWsRT20tsXFdgupJlRO8tmOwed8hbGpqaXXCq\nTpQFt4pGb87AMHQ8v47rxZCmqJqKZeioKli2gW1DlKaoisaGQQNNU0lIUTWNIIyIErANnZylk7N1\n/CBi+64yXpQw0p/DdRXKTsqm/hypqjNT9VEUKOSy89HzIyZLbvOiCGCbKr05GzdI0GaTh0yWPOru\nbLdeU4fEYIIYTVGxbZO8bdBbsJrdgec6UDcv0T0Oph75fjZAXFUV4jSh5gWEkYZl6BRyOmGUjSMz\nNBVb10hzKaVaSFx2ydkpBT8mUVV6c1nCtmrdY6YcoLsKEyWPMIyJUyjXfPqKJlGkESUpZSebgcA2\nVFIUwiShUgtBUcjldNb32TiuQ03R6A9j0nqKHyYkScJMxYMkRddU4iRLpFN3Q/p7bMo1j3LNJ45j\nwjhrATmYzLOH+jlKgC5E55o/NdYSLefSrb1jLGdu78Va09XZ3Cjr9suG7ocxXhhhWzooKeVqkI0J\nrwds31POepypCnlT46hBm5mqh+v6JLbF8zurjA4XKBR1bN0kjFL2TDoUcjp1J6KihJTrHiMDeXZN\nVAGN3qKZjVUPsnnTgyjCjKFuhSRJds+p1H12jNcYn6kxXQlI04SCbWDoCn1Fi+GBHLalzx5n0PwM\nGvcyy1DZO+0Qxin9RSsbblYPAZiu7nsGVYBKPSBNa1nepKHCvOlXK9WE4T6LowYlGVyrdXRw7rou\npjm/ojZ+D4L5mao9z1t03f3XEytrsXFDdS9k71SdmhdkSaIsHc8P2TvjMlULKQUV0kTFDWKG+y3G\nZ8oYhkbNC4nihJoT0lewsyDb0jBUjThN6MnrKGmWpdwwYKZSZbg/n3Xx1TSCKKbmprheSM2NGOqz\nMDUVL4roL1oM9+Xo7zGpeTGlsktM1u23L29iGiqlqs90zWeox8bxI1wvpu6FOH5EnEIUZWOcGsF2\nSjovMAfwgoQknd/tDrIgW1EWr45LjZOyrKy7u4wN6nwv15rreBGuf+BkgmGUkDd1DE1tjjUD6C8a\nFHMmz2yfYWLGwQtiLF0jTVJKtQBNgVrNg8RkbLblulYPiZKUNEkZGcxRtDRCXSVKIQhDBnosijmD\nmhOQpBo5U0XXDV7YU2WwaNJTNFAVFdfPks4FYYqqJZRqPjkrSyzXk89eGDy7s8RA0caydDRFYcNQ\ngcmSx3jJwXWjbOxdktJbzMYFHijz7MtZ7jhGIURn8JdoOZ835lxazjvG3KDx5Xo5NZ6RUtIsuXCs\nNOf9XmzdIEpQFQVVVQijhJoboSgKaeJlrcm2TqooGIaGqWv0F23GSy66qjBT80nShJkkwQ9SJis+\npAlD/TYpCqoy+3LIVqi7IVGUEMcpSbIvUaGpa6SpQjSbuG667LJ7vEK5HjSnY9N0lWJe56jhApV6\nQM2Z/0K5kNOz50YU8raBZehoSkzB1hnsy+GHMXEtaSavi+KEcs0jjFPCMELTFdKU5ovp3oKFrsTs\n1dKDDsylF9rq6ejg3LKsBcF14/dcLndQ6x5KMjjf93EcZ9n/bimu6877bzdtt1L18OZM6ZWkKVNl\nj5yVZZKcqfrkbR0FKFddSBWiMGGm6lN1Alw/QFVVqlUfy9AIo4iCbeD4PsW8Sc0NMbWY6arPUUMF\npioumqKwY2+dYs4gDCOKtkGUJARxQhIlBGkKbojjhbxipEjeNvD9kJydp5CzCcIESwfHjbBMSIkI\nA4U0jenP6dgmhEFIEAREscLEdB1FnR9sex6YpoquJs1kHZC18LuBB8kiNxjdgDTB97PEbr7vY1sa\nampQq0WMT1bZPVGft63+HpPenHJQ5+Nqng/5fHsmn1vpujrXSn2eSZIyNu00pwmErFvZUYN5fN8D\noFpz8MKF50ypnLVA752qk6ZQqXvkbQXTtLF0DctU6bF1Xtg5QxgEVOs+FScgiVMGe+1sbJ6hkrdM\nnts5g2UavDTmoWsKYRiTsw12TtQJwijLWKuprB/IMzHt4BVtyjWfV44UMTWVybJHFMZU3RAFCOOQ\nvGXghTGKqoACBVsjCGOSJMH1IpI4xfMiSngUY528pTNTdak6sxlylQRdTZiq+mhqNvYutRQ8z5t3\n3h/sd7H/9Wjf8gRdOXCwv1r1Zy330c51FTqjvrZq+7KPA6tU5sxYkkbN7dt2iqJAmkK55qzY+bVW\nn1O71tfVrKtz6QroJkCM5y3+WatpSpoGjE26lCoOpVpAsVynr2CiK/G8gFFNU2wdpoMAxwkoVT1s\nSyNNYlRVZbLkkfZa5AyFcpKgqFB3Ilw3QFM1ynWfyZKGpSsYZpYw2A+y59q6EzLUbzM27VLM6SRp\nQs7W0VXQVTAMlRQNw1AxNNDUhFqtzu6xMuPTdXZPOqAoVJyAvKUxNlGnL2+gadk851GcYGgatqXh\nuHqz8afuhpBmQzjjOCTws5cSpqmSRhFT5To1J6RUy+59CjmG+yxmyjUMNaaYz6acW+qcTpIsUXEQ\nZS8W8rMvvJZ6blmtAH0t6tyhWum62tHB+cjICKVSiSRJmkkRJicnsW2b3t7eBetOTEzMWzY5Ocm6\ndeuWvd89e/awZ8+eQy/4ErZv377i22z1doNYYbK8L4t4GCuUagHDfTYQU/NSdvkRvT0FZkplCvkc\nY1M1DN3ECwLWaTalSp0N63qp1nyKeYOXxqq88qhedDWbTzxvaYwMDWTdeywbkpRUAV1RULVs/sry\ndPbG0NRVlCjF9XyG+2x8z6FaDbGP6qFWjoichDDWqFdc/AhqlWz+8ihR6CnkcFyHcik7FlVRyOct\ndo7tzbrpxtnLB1MDVYnJ2SauF5CkWbZ5TVNxqile3aRa99G1lLmXsOE+C0NLCWOFvqJJ4ExR8icp\nT2Sf40TZp+aleHPe/h+9voBfgeVcClfjfBgaGlrxba6E1aqrcx3u57l/HWmY6LMwtezN+vj4ngXr\npJAllglC6n6adZlLNWr1LDlioCrUVI1SqUK57qMpCpoCGgluEBJGKkoSkUQa47UATVcgDMnbGlUn\noJgz0ICaH86ONzdJ0pixqTobhgo4foyqwFTZxTJstDQkjEKsGDRFwTYUDC1hZCCHodsUTA1VCZjy\nIgLPI1IV0lTFMlIsHfImGHiUKimGCj3/P3v3HiVZVR98/3vul7p09XWmBwZGUGE0gDgGHpayEi65\n4BsMGPMmb1aAEI1JlJgVF1lKEF1RAq4lughRY8LtjYomPoosktcsnyjJa0RfUQQCCg+GizLMTE/P\n9K2u5/7+cbqqq/o2fTnVU9Xz+6wF011dvfep6tpnn9/Ze/+2DWEQc3guohEq1KsJeUfDqyrkbIPG\nnEIQKQRRgqEpGNqx/xYrvdcjAxYT2vLb7yzWrfPpVtXRq20V+qO9Hu/ypY7lHZxeuOl29Mhhijvs\nVvmmruAFCS/tP8TTT2d7Yd/t96lX2+tWtNX18CKFySmfME4o5U0alaM8/ezRjn60KQGUELx6RBzW\nqfkxkadRr6rpzEyrSN4IOW2Hzf4jCeVaQN7RmCo38EOoxj67RnJUah6OY6CpOnEcMFJK87SUcgax\nkrBnpEgS+RiagUJCHEWAQhxFqIpK4pf50Y9nePFwzOR0nUo9vSltWwZKEhEFDX524DDTcx4kEbap\nzP9cZ+egTbWe9mUxGkdn0xv5pbzJ1PzrHRuwmKrDwcM1ohjmKg10TeVIUsFWKqhETB0xyS1aKdD+\nmU6A2VpCrRG0HnNtA8dSOXqM65Zu2Ypz00Zk2Vb7Ojjfu3cvuq7z+OOP8/rXvx6AH/zgB/zcz/3c\nkueec8453HnnnR2P/fCHP+SP//iP113v+Pg4pVJpYwe9jHq9zosvvsiePXuWjPj3e7mLRwVnqx6j\nMZQK6RTVOEloeBGqpmDbOQ4fnaJQyDNT9nBsC9+POXlHif2Hy619JcdH8igq5ByLJPEJEoXnXp5D\nVWC67HPyaA7fC0lMA03RsDSFk3fkCfwYVVeZnKqTyxlYGhSLOXRVZWjI5ZSTh9C0dFr6gOKn21go\nCgVHT5Na6QpRlE4xVhSYnqvhexVCihyteIRxumd0omnsGHJ45ckD1BohDS9qzRjQNZWBgoFdTqfo\nDw/YqIrSccdx4f09tfX+Ts01KJT91vsVRBGGprFz2GWktLbZH938PPSqrNtqu6zez+bfdrFSwcQx\nEl588UVeedopjNaSjrvUyfz/FRQSEvwgplINULV085WJow0aQUjDj/CiBMvQGRs2KBVjJqZrlAo2\njm1yeLqB6kcoioprmxw4WiMMI3w/YbRkp9lmHYOpcoPBvMVoySGOlXRGiALleki5DrmcQ7HoMFuJ\nODJTp5A3KBUsbNPAtWN0zURRNBS9Ti5v4Fga1XpAohjomkLOtcg7JkNFC9vU2DHktj7vYRQxWLTT\n9aPz7bJcC7DmX7/necxMT3L23j3kVrl7vdoshWPd7e9W+9nKOnq5rUJ/tNfjVb7UsTrtZzPAYQBO\n3X0S+Edb5eedSbzAI18cZO/eMzKpb6vep17Vzba6EVNzDYqDPp7nceDgAXaN78KyLEoFk6Fi5zVS\nOh07YGKqgXNgFl0DQ0/7o1IJ9pyUp95I87bQmmuOAAAgAElEQVTYFTg47ZHP2VS9GFVLlyu6jkkQ\nJeQtA9sy0NQatmVwYLLCdNnHtXUcQ2f3zhyOY2JbBtPlgCCKUdAwTYNcvgSKwmz1CH6oUJnfpi1R\nYnIlm0gxKBTyTM6VsUyVwWEHDYUwjtk5PoRhKHjzyY6Hyn7HNaVlqSiJQjBV4+Q4x2zFZ6BYZLBg\nYRpaa2na2KDTMXK++DNdqQUcnl76OTR1leJgvOTx5d7vrGxFm9uorNtqXwfntm3z67/+63zoQx/i\nlltuYWJignvvvZePfvSjQDoyXigUsCyLX/mVX+ETn/gEt9xyC7/1W7/FF7/4Rer1Opdddtm667Us\nqytTjRzH6dly29eWJIlOso5yT3Pd1u8Ol2IqjaC1vQWA68DooE0pZ+I3qgSJiutY1BohtqGyf7LM\n7rECvh/hOAZTszUKOQc/iNBUlQNHqpQKFgcmKzi2wdE5jx1DLkfn6jijLnUvxtYjdoykQf/ggANJ\nkk430jRsW2eomCOfs6l7EXkX6m2xUj5v4VgGKpAo4PkR1ZqfbpkxW0VVNcJEIYxAUTWiRCWKVHKu\nw84Ri0rdZ2qugaLpkKRbY5SKBihQcNMT2XJrddrf3zDRaARq6/1qKg04uO761sp263PWi7rVVttt\n9v1s/9u2Kxac1lTrnOsyPOR0rO+q+yHlysLdbMeGQj4hDGPKVY8w8dB1gwHDwAsSZis+CToJITnH\noVoPKNdDdE3FcXR2DhXYf7iMY2nU4oThkk21ETI84JAkMYVhF0VRyTkmc/UGjp126JqqYJoq4yM5\n/CBitlbFdQ3c+Z+X6wEHj9QZHSpSzOuYhsZ0JWC05FAqOuyfKKOgknMtBvIWwwM2pQGbMEiIohjX\n0SkVbHYO51q7FVTrPlOzAa6tMzSQfv5rjYAEo/W3WGk9XPv5aCPr5Lai/ZxIbbRdP7TX412+1LE8\nRV2Y1l7IOVT9hfJdx+TonEcQKZm/JmmrvWFxP2pZVjqLttB5jbSQFBRc12LnSJFawyefMykVElAU\nLFOjXPOwTZuTd2okMcxUG7xq9yCHZ+tEYYKmpjvs2GaaO2WuliYRDsKYwaKFQrpTjx8qaLrB5EwD\nXdeIkzjN3D7TYKYaESUJR8se5YZPzjZpeAFJkq5RL+Zt4hjybpohfqYS4lpp39oIEgo5l9FBgyCK\n2b1TRVHS/dANTaVSDzh4tIqmGxTyCoWczcR0jUTVSBQV17UZLDqMDC1Nhtr+ma76NWx76Ui4riuo\n4dLHF7/f3XAitLm+Ds4BbrjhBv7yL/+Sa665hkKhwJ/+6Z9y6aWXAvCmN72Jj370o1xxxRXk83k+\n85nP8KEPfYgvfelLnHHGGdx5550bWnN+olmc4bjRaDBbS4jjtU1daSabaJYVL8qW7Fg6BddCI+Jn\neQUnXyBOFBpexFzdY6joMF32sW0dXVPJuxa6puLaOrVGhGGoQMJA3sIyVVBVcraO4+QouiY5G3y/\nQcMLOHV8gCgKmauFlKs+igp5y0BTwPdDEsCy9Pmy02NM97g02DHkUvMCvCCi6ho4lsIz0wlRnFBw\nTYIwQtdVCq5BzjXSpCPzr73uh0wcrS1Zfz425K4pEdV6MpeK/rLa37Z9jV17O2oqE3R8ryoKJ43l\nOHAYHKeOoSlYhkY8P85umen68ij2UFUdFIXpch3H1PH9AFVTGXJMzjjFIYkjDk9785nUFQo5i4Kt\noxk61bqPZajEScLw/F1y09BBUbEMFUu30ptYjYiim9ZT9QIKOYskgYJjkAAa8Krdg+iaSmkgvZM/\nlHdIgBm/ke6mkCQkSWcyN39+P+NaIyTnLIyC+2H69bGyskvyNyG2Fy9sTwin0bYCvbWdmiSE276a\n/WijsfDYctdI7f2IgsJIycbzDfKuwWDBolwNmJxeWEs/4FrkTzN5+XCZMIrRdRXPj0jimLGcxZG5\nBnlH45W7i8zMBUT5BEVNp6bbhspQ0SKJwTB0JqdrhGGArseMDro0vABF09FU0DUNJUkYHnBwTI3x\nsTxJHFH3FcIwwTY1bCPd9lRXVUwjzd4+oJqMFhYC1ea2bC8fLjNV9tDm88c0/AhFUVBRcAwdZ352\n2rFuTK+0pV0pn26fKtek3dH3wblt29x6663ceuutS372zDPPdHx/1llncf/992/VoW0by2U4rjUC\nao2Q/Dq31l4tA6eqKjgGjA7mUFRjftskH1PTGCpGHJmtE0YJhq4yMuCQd9LRZ89PM0InpNuXRXGM\nNqgwMxPgmiG6lmaIH3Ud8q6JadgYukfeMSkWDAq2hR8mHJquAwqOraJqkHd1VEVl10iOHfNbThT1\n9KLeMjwqlTR7p6GpqEo6bbiUS6cMWabWcVKLwqQjME/fw5B4mTuP633fRH/b6N92paC+4FqMj8J0\nxWsljBkpOdimTj5nUG9EJImKH0UEYUwYxtRrVYYGC1S9GMvUMFQFyzEp1yLGR1yKOZM4TlBUFU1J\nmM2Z6LrCYMHGcXRMPV3KUa2H1BsuR+caRAk4mkqlETI1U6dST5irBNimRt5NR8MNXWOkZLFrpIA5\n32biOOHITIOcbZKbf13NzLNNpr4wQhIEEc2m1txCSbKyC3Fi8VfI1g4L26lJcL59NftRQ03XUY8N\nLj8qvHiXD4V0C9ucYzCQtym4FqqqECZpjiLHSmeAFXMGpqHh+xGz1QaqojI7v5xRQWU4r6MrDfwg\nYtdoDtvQMQyNncM54ihG12C0ZNPwNWzTQNdA0zWCMMa2DMxGQBhBzQtwbYPDR2s4toFjpoM8fhQz\naGloqopj663dWBa/nrmqx8uTFWYqHrPzCeA0LcGxDGxDY+dIvrXzSc0LWte0K1npOqOYsyjmrFWv\nWySb+8b1fXAuum+lLYv8cGNbeK02cmVoSdqRKjq2qadbMqka5WoD00w73MGChWFoBH7MYN5ges7j\nhYNzRGFCGKc/j+N0XaqSgO/H5G0NlQTT0AiDiGT+hOxaFqqqkgCqAkdm6lj19ATYrGt0cOndxbxj\nYlsaupbgWBpBDHGcZuO0TI2hgtNxB1HX0qlS7VurWaaGpq39RCUjftvXRv62qwX1xZzF7rECU+U6\nfhhj6iqvPHkQXVPYP1HG0FTCJKJSCck5On4DgijGtQ1UFWbrAUXFYM+uAgMFi+Giy1zVJwgj8rZB\nrRFT900cS2PHcI6Rkk3eMTg4WWN8BKpeSBBGxPN7mFumTpQkBGHCTKXBmYUSYRyTM0xQVExTa+1H\nOzmzMGoRJwl1L8APY/KOjopKIwjTkYskAQUMQ4M4xLWNVhbZlc5Zsu2gENtTe9+6eKvS5nZq7Umt\nxPajqgp51yBnplPBV9t2baXHm/t/J0nnzZzRUo7xkXRHoHw57attS+eliSpeEOJ5Cq6TLrNyLB3H\nMsi7JgN5G8dUGCmmWdyTBCD9fqzksP9IjdGSg6YqHJ1pYLoqA/l0ZlkYJZhmmu1950geXVcYHXCw\nTK21NHTx65mpeNQaIYahYZsaNS9kesZjpASDRTvdbs4LsVr7ph/7PV1t8GCl65ZjzV4Tq5PgXBzT\nSiez5ihVlhRg55BLrOjU/ZDZssdwyUZTIU4UcvNTwafmPOpawFQlQFUTxgYd5qx0qo4SKyi6wmkF\nCxQFU1fx6nOMDuYoujpBpKNoCpauo6kKzbHrJEk7cdcysAwVY34EfLm7i6qqsHPIZXLA4qSTBtB0\nAz+KUFAYLFitu6+QnqTCKEHXFBRLS5PfGVp6J9OSJig2bqWgXlUVThrLM1AwOzpUSD/nKBUq9YCG\nETMyYGPpAa5tsnusiKlpHDpaZWjAYuegS6KqBEFMKW/i+ekE+ZPG8sxVfQxd5ZSxAq86ZRAARVHI\nVXVGh2xe2D9HrREwV20AMYOFHIqSbsdmaiphkOAFIaZud5xjml/HScKRmXrrotsyVY5O14iTdPp+\nQjpddXjQRosjGnNKq80d6wJMCLG9dIyc6zJyLpa3liWCqwWk7Xuqx3FMqWAxMRUSxAkDOZNTdxYo\n5C2URCXn6gzmbeIk5uTxPKqmMluGgUKOXWMuuZzBeJJnxqrjOho518TSNHQjAUUlChPyto47nyi1\n4JqoirricbdTUCjkDPwgwjZVohiOTnvMVgJK+XQJ2WhpbUnVNjJ4sOyMWy/g0NFqOoglI+mrkshA\nHNNyJ7P2UaqspXc/0xPBnOKnmdcT0DWVOE5Hv0ZKDvsPB5SrPqahUcor+EGMoqgMlFRcyyKK0um8\nqhpx9EiNXcMOe8ZLaJrCXMWn7gccmUm3grBMDUVTUEjvvFqmhueFVOYDkOVOIqqqYGoJIyV7xeQU\nzbuHNS8gSaDhRVimhpM3yNmGrM8RXbNSh7prNE/eNZitemkOiDDgyWcbWLaDY1vU/HRLGBQ4Oudh\nGOl+qtr8+vWEdH2nqqsMDdgU8ybTlQaWoXHarhI1L0gz0LsW5YrH0y8eJQ4b6KaCaRgYWkjO1YmT\ndMu1xbNM8o7JnOlz8GiF2Uq65r2YM1CStH0Wczq6unDzLGcZ6PMXLJVaQNWvYWgqtqnRaBtNk/Vw\nQmxfXpAue1GUdPvUdrLmXDStdRnZSv1n83p4eq5OrRGzazjH2KBDkiSYpsorxgco5i38MG6V/dND\nsxRdi2RYQcdjdDjNh6Qo6Zr3gmtQrfkUHJ+ENKnrXM3Hj2IMU0PXVQZciz27BqjUfWYqad+dkDA5\nW8Mx9dZrKOWt1ixNL4iIExjI28RxTBhBEMR4QYhj6ShdjIsXj8onJEzNNpipeAzm0zw1MpK+MgnO\nxTEtPpkVHaVjlKpb6n7I1GyDWiMkIaFaD1BVyOXSKUNhmOCFEXUPDFWlkDPx/IA41jg6V8fQdLzD\nMcW8hpokFPJWa+34SMllruqhKGWiOF2PU/cCsME01Va9kGbcPKhVN3QSad49VFAYGrDJORFBEFFw\njNaxCLGVVFWhVLApFdIOslarMXlIxcg7qKqOGajUaiFTcw00TWWu1kABwjim4BqEcYLvxxi6ypHp\nOr4XMTxoY1sGrmUwPpLDCyLCMMEc1BidqXF0agpV0ci7Jnt2FRgZsEkSjd078oyP5Je0gySBhh8R\nRzGxopCgEIRROstFSZPTNXlBhKonzNYSDk/XW5llrfnkOUEUy116IbY5L0j7a9PQUBZFHc1p7RKc\nC9jcEsHm9XAQRdSDCF1VqNVD/DAmitKteFVVW3K9GMfQ8NOEwg0/oBCbWLqObRoopMseURSCIGa2\n2kBRFEo5CxXI2wan7CyiqgrVeogfxK1ZZemOJXYr0G1f0nZ0NkYrqBiGmgb9QUIYRhRck5GS00qs\n2g2LZ6l5XkitEbZ2VwHJA7MaCc7FmrSfzGq1GltxidueRE1BoZg3CYKYvGNQcAyGBiwOHq2iAOVG\nQBjGjJRsKo2InGVRLJgkUUKtEXDSUI5TdywEAc0ApT2hxQ7doVwNmCkvBOaWqc0H7hs7ibTfPWwm\nHrFNHd1QJVAQPUPXEgbyJigGakNhtpzOGGkE0fw+6lAqmhi6imvqaJpGpeYzPedR8+qESULBjWAg\nvSHV7Jg1ReXU8TxhI49i5Nk5WmC4ZKOi4lj6sje8WjcBcybhfMLEeiPEyKWJeUxdba2bC8IY19aJ\ngnDJetLlMtkKIbYnf37kfLnlds1p7YuTsgqxEaqqMFS0CcOEasPvCHINQ1tyvaipKlOzHpVaSL3h\nU6mFaKqHZWodA1+jpXSL04kpnZ0JKEqCaaTJ3xpBCEEa0Na9oLXcq7ljiYLSqrO5pG0gb1GppVuz\nHZlpYBrpzatizkz3Qt/gMq+1JHpbPOM2COPW9XQ7yQOzPAnORc9anEQtXUNjMlx0iEiIkwRTV5iL\nIzw/xNDTLSeqNZ/ENlCqUMxb7Bi00dUKur50L+nFd1ALrkUYx9SDqJWpU52/C7+Rk4isfRW9ZLlO\nFTpzPTQ8Aw2Fn02WW1NFDUPFNgwMXSVnG6iawuGZOs3dFIMwbl0keEHEcNFpdcyaomKoIWeePojj\nOFQbASgJmpYG4os79mY7cywDywwX2r+iUCqkx7v/UBk/jMi7JpV6QLVWXzJa1l6WEGJ7a645t4yl\n/XwzOA/CON0OS1v6HHHiyCKLeGt6e2Vh7zZ3Pot6QsLUXKNVfhhGFPMWURhSNzXytp5OfQ/iJdeg\nQRRTzFkLN6CDCJKEuh8uXIv6EXU/IIgSDE3B801scyHBW7PMvGO2llU2twduBsgbXea11kRvi2fc\nurZOpR60XkOTXAsvT4Jz0bNsS2ek5LSyNTeDZUNX+ckLUzz1/FHS6EBhsGhTsPV0bTcKtUaEoqTB\ntqFrOIsC8zhOmKt6zFTSNeelfLotRPsd0cU2chKR/clFr1ipUx1wF2aT5N20HQwVHWISXojKrb3S\nFYAkIYxj4hh0BYIwJIoSBnIGCQnB/MVIe8c8V44ZHbDSrVsaIUdnGstOx1uczE1VlI72v2PIJYwS\nDk9XmWsEGJpCM5tjEMTEydILbun4hTgxNAOT5hZT7drz49S9kIIr/e+JKqss4s0+TlXgkFJt5UAB\nmJptYFta6zpytuIxUDAwNIcoMBgbdsi55rK79aR5XZKOpZUApqkxNugSJwmVWsBcJZ0pVgfytk8h\nZy7p79r74WLeJA4TNE3BtvRj3pBY6QbGerYpbb/xsNz7nsW18Hbdrk2Cc9Gz8o5Jzk7vtDX3O7YM\njSOzdQ4eraACzRh6rhqgaxqWZVDKK8xWfJIEgjDCNi3M+TuZYaLhWgaHjlZ5ebLSMX1991iBk8by\nmQbUsj+56BUrdarGMp9FXVd5zStGsC2d6YpHpeqTAKapE8cJ5VpAwwupeRG2oVGupQH0aNGmMV9H\n3jEp5ix0JeIl0jXkEcmq0/Gav7ek/ZnzieiCCF1VKc63RT+MqXsBtqWhaUszNMtNMCFODM3zyuJt\n1GBh5BzSJTISnJ+41hNcHouqKuwYzhG3bbvW8IJWHqMmTVcwNY1Q19DVBFPXyNkGAysknJtIah2B\neRr0N/vpdAvRZsJT19aI4/Tx1fo7VVFw3NWD8jhO8COFIzMNvNADhdaWbc0bGBvdprQb18Lbebs2\nCc5Fz4jjhCBS2D9ZwTTT7R52DLnUvDSJhqmrlKsBB45Uma2mwYCuqxQsA0UFU1cIYxgZcBgqWmia\nymDBYnjI5KcveOTKPo1AJU6qeH7YcfLz/Iipcp2BQhpQZHkSkf3JRS9YqfP0w87H2+9Ejw265GyD\nw2qtY2QABUgSCnmbKI4IogSFhCBOmKsGzFWDVicJEERJq64wWpha6gdhx3Q8WOjEyzWPlw9X0VQV\ny9SYnKpR9yPyTueaNT+McXSFobzK2KCDohtyE0yIE0xzWvuxgvOaJIU7oW00uFzJ4qBT1xVMU++Y\nvu1aJpqqohJj2yZ5x2B8OLfiNqgDeZOaZ3XMGFUVhSCKGSik0+FLeYukbV36QMFa0t+tFLy2X1e3\nL287NFXjyKyH5tSoeklrdpuC0rqBsZmlmllfC2d5o6XXSHAuekIcJxycqvLTSY/ZsIxpeh2j2cWc\nwlzVo+GnI3VRGOMHEWEUkXdcSrpKqWASxRBGMa5lY5kaw0UbJek86c5WfeJoaZZKP4zTrSe26TQZ\ncWJr7zzbE6rpea05O3zZztwPInKu2XGxoaoqQwMOpqERzG/XMlv1iJKF5SDNPU3DwCeMNcpVj7l6\nzNE5r1WWbWoUcsmy0/EURem40DZ0lZmKj+torfVzkCaIsy2FhpaQd40VtzUUQmxfzYRwywUJi0fO\nxYmrG3mA2oNOy/A4PFVf8hxb1/AtDVsH29I6rinDMObwdI1Kw0NFRVXSbUZLeas1ct1+jM3Ewu0c\nc2k4t9Je488fmOnYL71sBeQcnYaXXisHUQSordltzboW55Np1X2cZqllfaOll0hwLnpCpe4zVwmo\n1gNKg+lji0ez0z0bE6IoJueYTFc8Ii/BMnxOP7mYTmtNFOIkIQjTrO4jww7TU9WOukxdpb7MFhKm\nrmJo6radJiNObM3p4jUvaK1ns0wNrRYzW0taN6UWd+ZxHFP3InL2Qudr6iqaqrYuEspVDwUFcz63\nQ3PN3PRcg2qtwU8PlRkeyVH3I3w/wjQ1XEsniGKUhGU79sUdrGXpuLZOECatbQlVReGksRwaEbOT\nXXjThBB9oX0rtcVce2G2jWyndmLrdh6g5cqHBNQ0d4uhpTejG35Epe7jWgZPPXeEmbLHXM2j1ogo\nuDoDORujEbZGrtuPca3Hv1yQ6nkh9SBq7TUOaZsI44XnGpqGP79mNAgWgvPF+WSO9wDWdk64LMG5\n6AleEBEsEzA3R7MhbXB1L8ALEnaNuJQKJuW6z1jJ4eTRPEmi0AjCzqlA8dIThmMZuKbOVNnrWHM+\nVHCApZ33dpkmI05szU710NEqMxWPoQELxzLwPY9aI6DWCFH0ZRLUWDqe39kmhgoOipKuI4d0VLt9\nm5TmnqaOpeEHMUmSEMURRdciZ6c3zhzXwDY0ivnlO/bFHayCwtCATd420A2146KgVqtl9TYJIfrQ\nmkfOJTg/oXU7uFyu/LofUq4ES57rBRGVWsB02SOMYmqNtD8t10IGi+kIu2moDBbsjmNc6/Ev1xaC\n+evjxdomvaXJ7JI0P4wxX0b7DYBeWaq5nRMuS3AueoJlaBjLnDBMXW2dYPKOidY6ASm4lpGuLx+w\ngQQVlZxmtpLHAfOZKTtPUDnbYMeQy2jdZ7bqkSQwWLAouBZH55ZOR4LtMU1GCFVVMAy14655kx9G\nFJ2ljysonDxWQFWVJWvUmhcIIyWbcjVotZPmnqZKWzZaXVNBgZxjkssZrWOwreW7oeU6Xtcy2DEs\ns1iEEJ281przlbdSA6g1lgZJ4sTS7eByufLLLP3cWYbGTDndii1ctNSy4YeMDeZwbWPVLOirWa4P\nzbsmS/ciSq+Bw8BPy1cURkrpdfVAwcIxj53d/XjopVH8rElwLnpC3jEp5g1ybcmemqPZ7XfrTh4r\nAApBELUSVCkouLbRkeCtybZ0XNNlcsCiVDApzpenqgqlgk2p0BmMbOdpMkLAyp9lU9dWvBPd3GZw\nsfYLhGLOWrKnad1buCAxdJW8axCESevO/Wp3ubdzxyuEyFYzW7u1zNpbW0bOxXHU7FcbC1uit/q+\nih0A1fTmdRt3fhbaZtfCL+5DXctgYqq2pI8vuBYaUdu1stsX/W2vjOJnTYJz0RNUVWF8KMepoxbD\nO4oYptkazW4/ORRzFoOFcMmJZWzQXfaEk3dMGo0IU0sYKtq47uqNeDtPkxECVhiRtg1cW99UQLzc\nnqawMJKVsw3Ghl3URKGYN9e01+p27XiFENlq7jqx3Mi5piqtrackOBdbrdmvGmrE1BGTsUGHkaF0\nBtjYoMvhqRozZQ/X1ubXnBsMFuxMrj2X60NX6uNVVVnztbLoLgnOxaZlld1cVRUMLeGk0dyKGZfb\ng4e6HxKFCbqmUPOW3x5ivccho3Viu1v8GS86Co05pfUZzyIgbq/D1hP0aIY943kGirlNtaf1nmtk\n5wUhTgx+W26a5TiWLsG5OG5UVSHvGuRMyLtGqx/SdZWfO32Eyeka5YaHhprmZHGMrvVvq/XxCVCp\nBVT9mvSZx5EE52JTVtpHsZvZzVVVSUf/qgujf3PVAGe+3ryTroU9OldPs0smy62wWb18Ga0T21n7\nZ7xWq7GRlnqsC4NmHboSMXkwZnjA2dTd+NXONet9vlxsCLG9rCU4ny57ss+56Dm6rjI+mmec/KrP\na+9zDU2lUgtaSVlh8/1bHCfM1hIOT9ex7SSTMsXGSHAuNmW5rZe2Irv5SvXOVT2q9c5p7yTBsgkw\nhBAbczwC39XONcskmT9u5yYhxNZK5jNLw/JbqQE4dnq5K/uci360uM+tNnwaXtTaag0237/VGuGS\nhInSZx4fSxfnZMj3/W4WD8Btt93GBRdcwPnnn8/HPvaxVZ978803c+aZZ7J3797Wv/fdd1/Xj3E7\nWymL+Uaym8dxgh8pTM01mKt6xPHKIfVK5c9UvCUX5A0vIojkrp8QWVkt8F1Oc6rc5EztmG17Jes9\n12R5bhJC9K4wSmieUlYKzpsJtmRau+hHi/tcP4ypNcLWTammzfRvzbwNi61WZhwnzFW9TfXtYqmu\njJx/8Ytf5M477+TQoUN8/etf56677mLHjh28613vyrSee+65h6997Wt8+tOfJggCrr/+ekZGRrj2\n2muXff7zzz/P9ddfz5VXXtl6LJ9ffRqJWF1W2c3jOOHQVI0jsx6Fsk8jUFcdiVtv+UEkJwwhsrKe\nwDerqXKrn2uW1is7LwhxYvDbzjsrtW93fuR8uV1dhOh1i/vW5o4nQRBht+1QsJn+zdTX12fK0rHu\nyXzk/J//+Z/5+Mc/zpVXXolhpHcqTz/9dD7zmc9wzz33ZFrX5z73Od7znvdw7rnnct5553H99dfz\n+c9/fsXnP/fcc7zmNa9heHi49Z9lyVSNzcg7ZsceorCx7OaVejpFp91qI3Er1VvKL//3NDQ5UQiR\nlfUEvqtNlVuP9Z5rsjo3CSF6m7eG4Lw5rb3myT7nov8s/lw7loFlahhtj2+2f3NtHdc2Oh5brcz1\nzqATa5f5yPk999zDjTfeyJVXXtkKxq+++mpc1+XOO+/k93//9zOp5/Dhwxw8eJA3vOENrcf27dvH\ngQMHOHLkCCMjIx3Pr1QqTExMsGfPnkzqF6msspuvdwrqSvUCS9ac25ZGQ5ORcyGysp4tBzcyVW45\n6z3XyM4LQpwY2kfOl9tKDdKtHEFGzkV/WtznqorC7rEChZxBEMWZ9G+qqjDgKowNOii6ccwyZelY\n92QenL/wwgsdAXPT+eefz4c//OHM6pmcnERRFMbGxlqPjYyMkCQJhw4dWhKcP//88yiKwt/+7d/y\nrW99i1KpxLXXXssVV1yR2TGdqLLIbr6RKagr1bv4glxNDGYnN3V4Qog26wl81ztV7lj1rudcIzsv\nCLH9eR3B+erT2iUhnOhHW3WzWSHd6iRrVW0AACAASURBVG2l7YzbydKx7sk8OB8ZGeGFF15g9+7d\nHY8/9thjHYH0Wniex8TExLI/q9VqAJjmwkhN8+vlEtE9//zzqKrK6aefzlVXXcUjjzzCTTfdRD6f\n59JLL133cTXrz0K9Xu/490QsV00SVCXtYD3PA9IRbzUxqNXWfxdOV0A3ASLqjQbQH+9Dv5a7lhP5\n8ZB1W23Xrfezn+pob2eNxvK/rxDg2karXcPm2vZyev196pU6ermtQn+31+3w+ejXOmbnqq2vkzhc\ntnxdTWfPVRtBJp+xrXqferW9drOtbsZW/F02KotjW0ufu1HrPT41SSAJOpakZt23b+b4tlLWbVVJ\nknVuAn0Md955J//0T//EDTfcwPXXX8/tt9/OgQMHuP3227nmmmvWlRTukUce4eqrr0ZRlt4Zuv76\n67ntttt44oknWkG553mcc845fPWrX2Xv3r1Lfmdubo5isdj6/uabb+aFF17g7rvvXtPx1Go1nn76\n6TUfv1ifBAgihSBKMDQFQ0s2tP+y2Hr79u073ofQQdpqb5G23Tt6ra2CtFexOS8e9vi/v5FOj3vn\nr46xa2jp8ppHnq3wtR/MAPCB3zoJvU/y0PRae5W2KtpJ374gy7aa+cj5H/zBH1Aul3nve9+L53n8\n4R/+Ibqu89u//dv80R/90brKOu+883jmmWeW/dnhw4e57bbbOHLkCLt27QIWprqPjo4u+zvtgTnA\naaedxve+9711HRPA+Pg4pVJp3b+3knq9zosvvsiePXtwHOeEL/fVp/fP8b7w4ouM7tiNpuuYuoZr\n65ueZtSPf7delXVbbdet93O71bHQrk9dUn4cJ9QaIX4Ybar9bIf3aSvq6OW2Cv3dXrfD56Nf6/D0\nI0AanJ/xqtMZLmhLyj/iHYT54HzPaa+k4G4uMeRWvU+9qpttdTO24u+yUb18bJDd8WXVr3fr+Loh\n67bala3U3vve9/LHf/zH/Pd//zdJknDaaaeRz+eZnJxcMXBer7GxMcbHx3n00UdbwfkPfvADxsfH\nl6w3B7jjjjt47LHHuPfee1uPPf3007ziFa9Yd92WZXVlqpHjOFJuH5Xb3CLKrsfYtkojSAjihPER\nN5MTUb+8D72sW2213Va8n9uhjsXlL2zDkgDZtJ/t8D5tVR29aDu01+3y+einOhR1IcN0qZjHsZeW\nXyou1JMoa1tTuxbSVntTL/9devnYYHPH141+Pcvj6xeZb6W2d+9epqamcByHs846i7PPPpt8Ps/+\n/fv55V/+5Uzr+u3f/m1uu+02HnnkEb73ve/xiU98gmuuuab186mpqdaamIsuuojvf//73Hvvvbz0\n0kt84Qtf4MEHH+Qd73hHpsckThxZbRElxIlItmERQmRhbQnhFgL4xecdIUQ2pF/PRiYj51/+8pd5\n8MEHAUiShHe/+92tPc6bDh8+vGRa+Wa94x3vYHp6mj/5kz9B0zR+8zd/syM4f9vb3sZb3/pWrrvu\nOs466yzuuOMO/vqv/5q//uu/5qSTTuLjH/84Z599dqbHJE4cWW0RJcSJSLZhEUJkYclWavHSc0hz\nn3OQ7dSE6Bbp17ORSXB+6aWX8uijj7a+37lzJ7Ztdzzn1a9+debblqmqyvve9z7e9773Lfvzhx56\nqOP7iy++mIsvvjjTYxAnriy3iBLiRCPbsAghstAenFuGhucFS57jdgTnS38uhNg86dezkUlwXiqV\nuPXWW1vf33jjjeTz+SyKFqJnubbeMVUOwLF08s7mEs0IcSLIOyZlK+iYAiftRwixXp6fBue6pqBp\ny6/WdK2FvlpGzoXoDunXs5F5Qrj2IL2d7/s8+eSTPbcthOh/cZxQqft4QYRlaOQdM7PEE6tRVYUB\nV2Fs0EHRjS2tW4hu2oo2paoK4yO549J2hRDbR3PkfKX15rBo5FzWnIsedLyuZbMk/Xo2Mg/Of/Sj\nH/GBD3yAZ599ljiOl/xc9kcUWVrIDLnQ2ZatgPGR3JacDBQg72aX+VWI420r25SqKhRzVqZlCiFO\nLN4agnNDV9E1hTBKqMu0dtFjjve1bJakX9+8zLO133LLLWiaxgc+8AEMw+Cmm27immuuQdd1PvGJ\nT2RdnTjBSWZIIbIlbUoI0U+awflq61oVRcGZn9ou09pFr5F+V7TLfOT8xz/+Mf/wD//A2Wefzf33\n38+rX/1qfud3foedO3fypS99icsuuyzrKsUJTDJDCpEtaVNCiH7iB+kszdVGziGd2l6u+TKtXfQc\n6XdFu8xHzuM4ZnR0FIBTTz2VZ599FoBLLrmEZ555JuvqxAlOMkMKkS1pU0KIfuK3Rs5Xv6RtrjuX\nbO2i10i/K9plHpyfeuqprW3VTjvtNJ588kkAyuUyvi/TM0S28o6JY3VOAJHMkEJsnLQpIUQ/aU1r\nN1efDNrcXUWmtYteI/2uaJf5tParrrqKG2+8EYBf+ZVf4dd//dexbZsf/vCHvO51r8u6OnGCk8yQ\nQmRL2pQQop+0EsLpq483NYOfugTnosdIvyvaZR6c/+Zv/iaDg4OUSiVOP/10br31Vu68807Gx8e5\n6aabsq5OCMkMKUTGpE0JIfrFWrZSg7Zp7Z5Maxe9R/pd0ZR5cH7zzTdz9dVXc8oppwBw+eWXc/nl\nl2ddjRBCCCGEOMG11pybxwrO02nt1bqMnAshelfma86/+tWvoqqZFyuEEEIIIUQHzz/2VmoA9nzw\n7vkSnAshelfmUfQv/MIv8PnPf55KpZJ10UIIIYQQQrSsdSs1ez5hXN2X7amEEL0r82ntk5OTfO1r\nX+Mf/uEfGB4exrI6109885vfzLpKIYQQQghxAvLWuOZcRs6FEP0g8+D8/PPP5/zzz8+6WCGEEEII\nITos7HN+jOB8Plt7GCWEUYyuyRJMIUTvyTw4v+6667IuUgghhBBCiCUWRs5XD7bttoRxDT8i70hw\nLoToPZkE5w888MCan3vFFVdkUaUQQgghhDiBxXFCEKZrzo+dEG7hktfzQ/KO0dVjE0KIjcgkOH//\n+9+/pucpiiLBuRBCCCGE2DQ/XEjudqyt1KxFI+dCCNGLMgnOn3nmmSyK2ZS3v/3tXH755asG//v3\n7+emm27i8ccf56STTuKGG27gjW984xYepRBCCCGEyILXFmSvNSEcQMOTpHBCiN7U9wtukiThIx/5\nCN/5zneO+dx3v/vdjI2N8ZWvfIW3vOUtXHfddRw6dGgLjlIIIYQQQmSpuY0arH0rNZCRcyFE78pk\n5Hzv3r18+9vfZnh4mDPPPBNFUVZ87tNPP51FlQBMTEzw53/+5+zfv59isbjqc7/73e/y0ksv8aUv\nfQnLsnjnO9/Jd7/7Xb785S9LEjshhBBCiD7TMa39GMF557R2GTkXQvSmTILzW265hUKh0Pp6teA8\nSz/+8Y/ZtWsXd9xxB29961tXfe5//dd/8drXvrZj3/V9+/bx+OOPd/swhRBCCCFExtqnta8nIZyM\nnAshelUmwfmVV17Z+vpYQXKWLrroIi666KI1PXdycpKxsbGOx4aHh5mYmOjGoQkhhBBCiC5q7nEO\na9hKzVoI3j0ZORdC9KjM9zkH+MY3vsG9997LT37yE0zT5FWvehXvfve7ecMb3rCucjzPWzF4Hh0d\nxXGcNZdVr9cxTbPjMdM08X1/XcckhBBCCCGOPy/YYEI4GTkXQvSozIPz++67j1tuuYXLLruMX/3V\nXyWKIh599FGuvvpqPv7xj3PZZZetuawnnniCq6++etlp8p/85Ce55JJL1lyWZVnMzs52POb7PrZt\nr7mMJs/zqNVq6/69ldTr9Y5/pVwpt9/KdV030zKzknVbbdet93O71bEdXsN2qaOX2yr0d3vdDp+P\nfqyjXFn4vCRRQK1WW7H8JElQVYU4TihX6pv6rG3V+9Sr7bWbbXUztuLvslG9fGwgx7cZWbdVJUmS\nJLPSgEsuuYRrr72W3/3d3+14/O///u/56le/yr/+679mWV3LxRdfzHve854Vt1L7u7/7Ox5++GE+\n+9nPth77m7/5G5544gnuuuuuNdVRq9UyTWgnxHaxb9++430IHaStCrG8XmurIO1VbNyPflbjf357\nCoD3XjFO0V199PzW//kyXpDwi2cV+cWzVk8k3At6rb1KWxVieVm21cxHzicnJ7nwwguXPP5Lv/RL\nfPKTn8y6ujU755xzuPPOO/F9vzW9/dFHH133VHuA8fFxSqVSZsdWr9d58cUX2bNnz7qm6ku5Um6v\nlNursm6r7br1fm63OrbDa9gudfRyW4X+bq/b4fPRj3UcbhwA0uD8tXvPIO8aq5bv2pN4gUdhYJC9\ne1+94Xq36n3qVd1sq5uxFX+XjerlYwM5vs3Iuq1mHpyff/75fP3rX+ed73xnx+P/8R//wbnnnpt1\ndauamprCtm1c1+W8885jfHyc97///bzrXe/ioYce4sknn+SjH/3ousu1LKsrU40cx5Fypdy+LbcX\ndautttuK93M71LEdXsN2qqMXbYf2ul0+H31Th7pwGVsayHesO1+ufMfSmS57RLGSyeuTttqbevnv\n0svHBnJ8vSCT4Lx9RHx8fJzbb7+dp556ite//vVomsaPfvQj/uVf/oW3v/3tWVS3rOXWpb/tbW/j\nrW99K9dddx2qqvLpT3+av/iLv+A3fuM3OOWUU/jUpz7Fzp07u3ZMQgghhBCiO9q3UjP01bO1w8J2\napIQTgjRqzIJzu+///7W14qisHPnTp566imeeuqp1uNjY2M8+OCD/Nmf/VkWVS7xzW9+c8ljDz30\nUMf3u3fv5nOf+1xX6hdCCCGEEFunuSWaY2nLDtIsZs1nbG/IVmpCiB6VSXDeHgRffPHFfOUrX2Fw\ncLDjORMTE7zlLW/JojohhBBCCHGCq3tpkN0cET8WuxWcy8i5EKI3ZRKcf+1rX+M///M/ATh48CAf\n+chHsCyr4zkvv/wyqnrsKUdCCCGEEEIcSzM4d6w1Bufzz/MkOBdC9KhMgvNzzz2Xf/zHfyRJEpIk\n4cCBAxiG0fq5oqSJNzaSfE0IIYQQQojFWiPnawzOZVq7EKLXZRKcj4+Pt/YPv+qqq/jUpz5Fsdj7\n+0cKIYQQQoj+1JyevuaR82ZCOE9GzoUQvSnzrdQk4ZoQQgghhOi2emOd09rnR849GTkXQvQoWQQu\nhBBCCCH6Tt1fX3BuSUI4IUSPk+BcCCGEEEL0nYVs7dqanu/IPudCiB4nwbkQQgghhOg7rWzt9vqm\ntYdRTBjFXTsuIYTYKAnOhRBCCCFE32k0g/M17nNutT1PRs+FEL1IgnMhhBBCCNF31r/P+cL0d0kK\nJ4ToRRKcCyGEEEKIvhKEMWGUAGvf59yWkXMhRI+T4FwIIYQQQvSV5qg5rD9bOyxMiRdCiF4iwbkQ\nQgghhOgrjY7gfG3Z2tuzusvIuRCiF0lwLoQQQggh+spGRs7bp7V7EpwLIXqQBOdCCCGEEKKv1NsS\nuq11zXnHtHZJCCeE6EESnAshhBBCiL5Sb2xu5FymtQshepEE50IIIYQQoq+0j3yvPTiXrdSEEL1N\ngnMhhBBCCNFXNrLm3NBVVFWZ/30ZORdC9J5tE5y//e1v54EHHlj1OTfffDNnnnkme/fubf173333\nbdERCiGEEEKILLQH1+3T1VejKEpr9FxGzoUQvWhtZ7MeliQJN998M9/5zne4/PLLV33u888/z/XX\nX8+VV17Zeiyfz3f7EIUQQgghRIaaI+e6pmLoax9rsk2NWiOUNedCiJ7U1yPnExMTXHPNNfz7v/87\nxWLxmM9/7rnneM1rXsPw8HDrP8uytuBIhRBCCCFEVpr7nK91SnuTNT/KLtnahRC9qK+D8x//+Mfs\n2rWL+++/n1wut+pzK5UKExMT7NmzZ2sOTgghhBBCdEW9FZxrx3hmp+a0dhk5F0L0or6e1n7RRRdx\n0UUXrem5zz//PIqi8Ld/+7d861vfolQqce2113LFFVd0+SiFEEIIIUSW6hscOW+uT/ckOBdC9KCe\nDs49z2NiYmLZn42OjuI4zprLev7551FVldNPP52rrrqKRx55hJtuuol8Ps+ll1667uOq1Wrr+p3V\n1Ov1jn+lXCm338p1XTfTMrOSdVtt1633c7vVsR1ew3apo5fbKvR3e90On49+q2Ou2gDAMtSOz82x\nyjfmB9qr9Y1/3rbqferV9trNtroZW/F32ahePjaQ49uMrNuqkiRJkllpGXvkkUe4+uqrURRlyc8+\n+clPcskll7S+v/jii3nPe96z6kj43Nxcx9r0m2++mRdeeIG77757TcdTq9V4+umn1/EKhDgx7Nu3\n73gfQgdpq0Isr9faKkh7FRtz7zcO89PDPmecZPN//cLImn/vH791hGf2Nzh1zOTaS8e6eISb12vt\nVdqqEMvLsq329Mj5eeedxzPPPJNZeYuTxp122ml873vfW3c54+PjlEqlrA6Ler3Oiy++yJ49e9Y1\nG0DKlXJ7pdxelXVbbdet93O71bEdXsN2qaOX2yr0d3vdDp+Pfqsj+F9TgM/J48Ps3bt3zeWPPPUk\n7D+Eplsdv7ceW/U+9aputtXN2Iq/y0b18rGBHN9mZN1Wezo4z9Idd9zBY489xr333tt67Omnn+YV\nr3jFusuyLKsrU40cx5Fypdy+LbcXdautttuK93M71LEdXsN2qqMXbYf2ul0+H/1Qx1w1AGCklFu2\nnJXKz7npLj1ekGz6NUpb7U29/Hfp5WMDOb5e0NfZ2o9lamqqtSbmoosu4vvf/z733nsvL730El/4\nwhd48MEHecc73nGcj1IIIYQQQqxVEMZU6mlwXiqsb0vchYRwspWaEKL3bJvgfLl16W9729u45557\nADjrrLO44447eOCBB7j88su57777+PjHP87ZZ5+91YcqhBBCCCE2aLbitb5ed3BuyVZqQojetW2m\ntX/zm99c8thDDz3U8f3FF1/MxRdfvFWHJIQQQgghMjZTXgjOB/IbGzmX4FwI0Yu2zci5EEIIIYTY\n/mbaR87XHZynI+dhFBNFcabHJYQQmyXBuRBCCCGE6Bsz5Ubr68F1rznXWl/L6LkQotdIcC6EEEII\nIfrGTMUHQNdUco6xrt+1zIUVnQ1JCieE6DESnAshhBBCiL7RXHNeypvLJgReTfvIuScj50KIHiPB\nuRBCCCGE6But4HydU9phISEcyLR2IUTvkeBcCCGEEEL0jZlKuuZ8vZnaAay2kfO6J9PahRC9RYJz\nIYQQQgjRNzYzcu5YsuZcCNG7JDgXQgghhBB9IYxiDhypArBj0F3377v2QnBeq0twLoToLRKcCyGE\nEEKIvvDSRJkgTPcnP313ad2/n3fN1teVRpDZcQkhRBYkOBdCCCGEEH3hv1+aaX39ypPXH5ybuoqu\npZe/lZqf2XEJIUQWJDgXQgghhBB94Sf70+B8qGgxVLTX/fuKopB3073Rq3UZORdC9BYJzoUQQggh\nRF94bj44f+XJgxsuI2fPB+cNWXMuhOgtEpwLIYQQQoieF4QRLxyYA+CVJw9suJzmyLlMaxdC9BoJ\nzoUQQgghRM/79hMHWsngXnPa8IbLyTkyrV0I0ZskOBdCCCGEED3v/3n4BQDGR3KcdfrIhsvJzwfn\nFQnOhRA9RoJzIYQQQgjR0554dpL//dNpAP6PN74CVVU2XJaMnAshepUE50IIIYQQomfNVjw+8cUf\nAlBwDS75+VM2VZ6MnAshelVfB+flcpkbb7yRN77xjVxwwQXccMMNlMvlFZ+/f/9+rr32Ws4991x+\n7dd+jYcffngLj1YIIYQQQqzXXQ8+xdRcA4A/+T/PbQXXG5VvGzlPkmTTxyeEEFnp6+D8gx/8IM8+\n+yx33XUX99xzD8899xw33XTTis9/97vfzdjYGF/5yld4y1vewnXXXcehQ4e28IiFEEIIIcRaPfGT\nSf7j0f0A/NJ5p3DBWeObLjPnmABEcULDjzZdnhBCZKVvg/N6vc6//du/8cEPfpC9e/eyd+9e/uIv\n/oJvfOMb+P7SrTG++93v8tJLL/HhD3+Y0047jXe+85287nWv48tf/vJxOHohhBBCCHEsX37oJwAU\ncybXXv7aTMpsH3mXdedCiF7St8G5qqp85jOf4cwzz2w9liQJURRRq9WWPP+//uu/eO1rX4tlWa3H\n9u3bx+OPP74lxyuEEEIIIdau7oU89dxRAH71gj0UXDOTciU4F0L0Kv14H8BGWZbFm970po7HPvvZ\nz3LGGWdQKpWWPH9ycpKxsbGOx4aHh5mYmOjqcQohhBBCiPV78r+PEEbpvub7zhw7xrPXLtcWnEtS\nOCFEL+np4NzzvBWD59HRURzHaX3/+c9/nq9//evcfffdyz6/Xq9jmp13XE3TXHYKvBBCCCGEOL5+\n8Ex6DZhzDM44ZTCzcvOujJwLIXpTTwfnTzzxBFdffTWKsnQvy09+8pNccsklANx333381V/9FTfe\neCMXXHDBsmVZlsXs7GzHY77vY9v2uo/L87xlp85vVL1e7/hXypVy+61c13UzLTMrWbfVdt16P7db\nHdvhNWyXOnq5rUJ/t9ft8PnoxToemw/Ozz59CM9rZFa+moStr4/OVNb9uduq96lX22s32+pmbMXf\nZaN6+dhAjm8zsm6rStLne0jcfffdfOxjH+P9738/v/d7v7fi8/7u7/6Ohx9+mM9+9rOtx/7mb/6G\nJ554grvuumtNddVqNZ5++unNHrIQ286+ffuO9yF0kLYqxPJ6ra2CtFexPD+MueVLBwD41dcP8D/O\nLGRWdhwnfPgfX07L3jfA/zgju7Kz1GvtVdqqEMvLsq329Mj5sXz1q1/ltttu48Ybb+Sqq65a9bnn\nnHMOd955J77vt6a3P/roo7zhDW9Yd73j4+PLrmvfqHq9zosvvsiePXs6pupLuVJuv5Tbq7Juq+26\n9X5utzq2w2vYLnX0cluF/m6v2+Hz0Wt1PP/yHJAG5/vOOp29rxzOtPycPUG1EeLkh9i795VdeQ2b\n0cvttZttdTO24u+yUb18bCDHtxlZt9W+Dc5nZ2f5yEc+whVXXMFll13GkSNHWj8bGhpCVVWmpqaw\nbRvXdTnvvPMYHx/n/e9/P+9617t46KGHePLJJ/noRz+67roty+rKVCPHcaRcKbdvy+1F3Wqr7bbi\n/dwOdWyH17Cd6uhF26G9bpfPRy/UMTl3tPX1K08ZwXXXd0F+rPLHhlxeODDHdDnY8GuVttqbevnv\n0svHBnJ8vaBvt1J7+OGHqdfrPPDAA1x44YVceOGFvOlNb+LCCy/k0KFDALztbW/jnnvuAdKt1z79\n6U8zOTnJb/zGb/DP//zPfOpTn2Lnzp3H82UIIYQQQohFXpooA+BYOsMD688PdCw7htIL/Imp3ls7\nLYQ4cfXtyPmb3/xm3vzmN6/6nIceeqjj+927d/O5z32um4clhBBCCCE2qRmc796RXzYx8GbtGMoB\nEpwLIXpL346cCyGEEEKI7emliQoAJ491J1nb2FA6TX5qroEfRF2pQwgh1kuCcyGEEEII0TOCMObg\n0SoAp+zoTnC+c37kHODwtIyeCyF6gwTnQgghhBCiZxw6WiWO051+Tx7Ld6WO5ppzkKntQojeIcG5\nEEIIIYToGc1Rc4Dxkdwqz9y4MQnOhRA9SIJzIYQQQgjRMw4dSYNzRYGdw90Jzh1Lp5gzAZg4KsG5\nEKI3SHAuhBBCCCF6RnPkfLhoYxpa1+ppTm1/6XC5a3UIIcR6SHAuhBBCCCF6xsH5kfPxke6sN296\nzSuGAXjsfx9mtuJ1tS4hhFgLCc6FEEIIIUTPODQ/cr5z2D3GMzfnl847BYAwSviPH+7val1CCLEW\nEpxvE3GcMFf1mJypMVf1WllOhRBCdJecf4XIThQnrQRt3UoG13TqeJFXn1IC4P5//wk/PTjX1fqE\n2I6kD8yWBOfbQBwnHDxS5fBUndmyz+GpOgePVKVxCCFEl8n5V4hsHZmpE0Zp++l2cA7w1l98FQBT\ncx43fPrbzJRlersQayV9YPYkON8GKnWfuhd2PFb3QmqNcIXfEEIIkYWVzr+Vun+cjkiI/tbM1A4w\n3qVM7e3eeM4u/vS3XgdAuRbwhf/1TNfrFGK7kD4wexKcbwNeEC37uB8u/7gQQohsrHT+XelxIcTq\nnj8wC6TbqO0a7W5CuKZLzzuVX3z9yQB8/bsvsl+ytwuxJtIHZk+C823AWmGbEVPv3vYjQgghVj7/\nrvS4EGJ1P3lpBoBTdhRwLH3L6r3qzXvRNYU4ga//fz/dsnqF6GfSB2ZPgvNtIO+YSzowx9Jx7a3r\n1IQQ4kS00vk375jH6YiE6B9JkvDA//vffO5fn6ZaDwB49mfTALxq9+CWHsvYoMvPv2YnAP/xw/1E\nUbyl9QvRj6QPzJ5Eb9uAqiqMj+So1H28IMIyNPKOSaNRP96HJoQQ29pK519VVY73oQnR8/7l2y9w\n94M/AuA/H3uZP79qXytTezOL+la6+A27+e6TB5kpezz27CRv2Ltjy49BiH4ifWD2JDjfJlRVoZiz\njvdhCCHECUfOv0Ks34HJKvf+y49a3x88WuW9t3+r9f1Wj5wDvGHvDgbyJrMVn298/2cSnAuxBtIH\nZkumtQshhBBCiC31L9/5KUEYo2sq+84c6/iZoaucOl7c8mPSNZVfODdNDPe9pw5RqUnGaSHE1urr\n4LxcLnPjjTfyxje+kQsuuIAbbriBcnnlDJs333wzZ555Jnv37m39e999923hEQshhBBCnNjqfsy3\nnzgIwC+8/iTed/XPMzxgt37+lgtPw9CPzyXqJT9/CgBhFPOtx18+LscghDhx9fW09g9+8IPs37+f\nu+66C4APfehD3HTTTdx+++3LPv/55/9/9u49SrKyvhv9d9937arq6vt0zwwwDggzMJKRAYUTCEYn\n6npzEsATyYvGazDmZGmMSyMBjeSE4IDxdQXFKHklE4GIK/LGREUMKjFCgjFEkMvMoFxmhmF6+l7d\nVbXvl/PH7qqu6q7qa1XXpb8fGC+gkQAAIABJREFUl4uZmqq9n+rez977t5/f83tewEc/+lFcddVV\npddSqY1ZpoOIiIiIgP9+rgDHiwuu/d+X7kRCk/GJ974W337kBVy8ZxivPW+oaW3buS2DHcNdODoy\ni289/ALe9NozIEltPZZFRG2kbc82lmXhe9/7Hj75yU9i9+7d2L17N2644QZ8//vfh+tWT0N6/vnn\nce6556Kvr6/0f03jHAkiIiKiRjFtD54fr3tcsDz8+6E4y/HcV/TirO1x4beztnfjj/7nBbh4zzAE\nobnFpK74lTMBACfG8njosZea2hYi2lzaduRcFEV86Utfwq5du0qvRVGEIAhgmiZUtbKEfz6fx+jo\nKHbs2LHBLSUiIqJWN5N3cNd3DuP4qVkM9SXx9jfvwlBfstnNahsFy8PXvvcsHn7iZUiSiMtfvQ09\naR3/+cwIfvaLCSR1Ga/bdxomsyYsNx41f+f/OLfJra7uVy88Dd/4t+dw/FQOdz9wGBedO4TudO3B\nnCPHpnDfD36BmbyDV53Vj2veeA4Umes8E9HqtW1wrmkaLr300orX7rrrLpxzzjno7l68/MYLL7wA\nQRDwxS9+ET/60Y/Q3d2N97znPbjyyis3qslERETUgp49NoVb7noME9l4CdIjx6bxX4dH8dG372PF\n7hUYn7bw53f+GEdHZkuvff0Hv6h4T8H2cf+/v1j6+4W7BnDezr4Na+NqSKKA9/7Gefiz//1jTOcc\nfPar/41PvPe1UJXKgDuKInzr4Rdw5zefRhBGAOJj5/Gfj+NP3nkRtvQazWg+EbWxlg7OHcfB6Oho\n1X8bGBhAIpEo/f2ee+7Bv/zLv+DOO++s+v4XXngBoijizDPPxDve8Q785Cc/wZ/+6Z8ilUph//79\nDWk/ERERtSbb9fH085P46bNjeOA/XoQfxMHVtoEkTk4UULA8/H9f/jF+7TWn45JXDSOTikdOBQEQ\nBAEC5v4rAJZl49S0i8RIDomEt+R+o2j1bY2iCLZtY2TKhX5yFrq+dBXxNewCiADbtnFyyoX28kr2\nEcFyfDx7bBr/51+fQ8GKv/f5Z/UDAA69OAk/iDDYk8DrLzwdIxMF/PuTJ+EHIc49PYH/96rz1tLK\nDbNv1xb8xmU78a2HX4iD7S88gt+4bCeGepNwHBvPHDfxz489gcd/PgEAUGUR3V06xqZMPPdSFn/0\n2R/iNy/biT1n9uPcnX2QuO4zEa1ASwfnP/vZz/DOd76z6tyj22+/HW94wxsAAH//93+Pm2++GR//\n+MdxySWXVN3WlVdeide//vXo6oqX5jj77LNx9OhR3HvvvSsOzsMwTsPK5/Nr+To1OY4DAMhms7As\ni9vldttyu7quQxRbo4xFo/pquUb9PDttH53wHTplH63YV4Hm9dc//fJP8dKYWXqPpoh4z/94JS4+\nbwCHjmbxxX96FjnTw/d+chzf+8nxFe5prN5Nb5t9CACu+pXT8Ru/fBoEQYDl+JjOuRjqS0AUBAAD\neOvrtmEyW0DkZOG7BUxO+nVveT370W9eMoQTp7J4/BdT+MVLWXz2qz+t+r6tfQl84P/ZjaG+BL79\n7y/hGz86jrzl4asPPgvgWfxfewbwe795zpq+Ryv1143oq+uxEefptWrltgFs33rUu68KUbSWZ7it\n484778Rf/uVf4k/+5E/w7ne/e1Wf/epXv4p7770X3/rWt1b0/snJSRw9enT1jSTaBHbv3g3DaI0U\nPvZVotpaqa8C7K9ES2ml/sq+SlRbvfpqS4+cL+cb3/gGPvOZz+DjH/843vGOdyz53s997nN4/PHH\ncfDgwdJrhw8fxite8YoV7y+TyWDHjh3QNK1lnmIStQpd15d/0wZhXyWqrZX6KsD+SrSUVuqv7KtE\ntdWrr7btyPnMzAx+9Vd/FW9605vwkY98pOLfent7IYoipqamoOs6DMPAU089hWuuuQYf+chHsH//\nfjz88MO49dZbcffdd+P8889v0rcgIiIiIiIiauPg/Dvf+c6ioDyKIgiCgB/84AfYunUrXv/61+Mt\nb3kLPvCBDwAAHnroIdx22204duwYtm3bhg9/+MMsBkdERERERERN17bBOREREREREVGn4IQRIiIi\nIiIioiZjcE5ERERERETUZAzOiYiIiIiIiJqMwTkRERERERFRkzE4JyIiIiIiImoyBudERERERERE\nTcbgnIiIiIiIiKjJGJwTERERERERNRmDcyIiIiIiIqImY3BORERERERE1GQMzomIiIiIiIiajME5\nERERERERUZMxOCciIiIiIiJqMgbnRERERERERE3G4JyIiIiIiIioyRicExERERERETUZg3MiIiIi\nIiKiJmNwTkRERERERNRkDM6JiIiIiIiImqztg3PXdXHDDTfgoosuwmWXXYaDBw/WfO83v/lNvOlN\nb8Iv/dIv4ZprrsGTTz65gS0lIiIiIiIiqq7tg/Nbb70Vhw4dwt13340bb7wRt99+Ox588MFF73vs\nscfwiU98Ah/84Adx//33Y+/evXjf+94Hy7Ka0GoiIiIiIiKieUIURVGzG7FWlmXh4osvxp133okL\nL7wQAPDFL34Rjz76KO66666K9373u9/FsWPH8P73vx8AkM/nceGFF+LrX/86XvWqV21424mIiIiI\niIiK5GY3YD2OHDmCIAiwd+/e0mv79u3DHXfcsei9b37zm0t/dhwHf/d3f4f+/n6cddZZG9JWIiIi\nIiIiolraOjgfHx9Hd3c3ZHn+a/T19cFxHExPT6Onp2fRZx599FH87u/+LgDgM5/5DBKJxIa1l4iI\niIiIiKiatg7OLcuCqqoVrxX/7rpu1c+cc845+Md//Ef88Ic/xHXXXYft27fj/PPPb3hbiYiIiIiI\niGpp6+Bc07RFQXjx77VGxHt7e9Hb24tdu3bhiSeewL333rvi4DwMQ9i2DV3XIYptX0uPqGOxrxK1\nD/ZXovbAvkrUeG0dnG/ZsgXZbBZhGJZOEhMTE9B1HV1dXRXvfeqppyBJEs4999zSa2eeeSaef/75\nFe/Ptm0cPny4Po0n6iD79u1rdhMqsK8SVddqfRVgfyWqpdX6K/sqUXX17KttHZzv3r0bsizjiSee\nwAUXXAAgXjJtz549i95733334cSJE7jzzjtLrz3zzDM477zzVr3f4eFhdHd3r73hC1iWhaNHj2LH\njh11nQPP7XK7G7XdVlXvvlquUT/PTttHJ3yHTtlHK/dVoL37ayccH52yj074DsV9tKpG9tX12Ijf\ny1q1ctsAtm896t1X2zo413UdV1xxBW688UZ86lOfwujoKA4ePIhbbrkFQDyKnk6noWkafvu3fxtX\nX3017r77bvzKr/wK/vmf/xlPPfUUPv3pT696v5qmwTCMen8dJBIJbpfbbdvttqJG9dVyG/Hz7IR9\ndMJ36KR9tKJO6K+dcnx0wj464Tu0qo3oq+vRyr+XVm4bwPa1grafMHL99ddjz549eNe73oWbbroJ\nH/rQh7B//34AwKWXXooHHngAAHDuuefiC1/4Ar7+9a/jiiuuwMMPP4y//du/xeDgYDObT0RERERE\nRNTeI+dAPHp+4MABHDhwYNG/HTlypOLvl19+OS6//PKNahoRERERERHRirT9yDkRERERERFRu2Nw\nTkRERERERNRkDM6JiIiIiIiImozBOREREREREVGTMTgnIiIiIiIiajIG50RERERERERNxuCciIiI\niIiIqMkYnBMRERERERE1GYNzIiIiIiIioiZjcE5ERERERETUZAzOiYiIiIiIiJqMwTkRERERERFR\nkzE4JyIiIiIiImoyBudERERERERETcbgnIiIiIiIiKjJGJwTERERERERNRmDcyIiIiIiIqImY3BO\nRERERERE1GQMzomIiIiIiIiajME5ERERERERUZMxOCciIiIiIiJqMgbnRERERERERE3G4JyIiIiI\niIioyRicExERERERETUZg3MiIiIiIiKiJmNwTkRERERERNRkcrMbQNQsYRghb7lwvACaIiGVUCGK\nQrObRdRy2FeIqFl4/qF2YDs+vva9Z5HQZLzlV8+CIkvNbhK1KQbntCmFYYSRiQIsxy+9ltM8DPcn\nedEnKsO+QkTNwvMPtYO85eHPv/xjHD46BQD48TOn8GfXXoxMSmtyy6gdMa2dNqW85VZc7AHAcnzk\nLbdJLSJqTewrRNQsPP9QO/jK/YdKgTkAPPdSFnc/cLiJLaJ21vbBueu6uOGGG3DRRRfhsssuw8GD\nB2u+94c//CGuvPJKvPrVr8YVV1yBhx56aANbSq3E8YJVvU60WbGvEFGz8PxDrc7xAvzo8RMAgIv3\nDOGXf2krAOBfH3sJM3mnmU2jNtX2wfmtt96KQ4cO4e6778aNN96I22+/HQ8++OCi9x05cgQf/OAH\n8da3vhXf/OY3cfXVV+MP//AP8eyzzzah1dRsmlJ9LlCt14k2K/YVImoWnn+o1f3k6VMw7Ti74zcv\nOxO/vf9sAIDrh3jg0aPNaxi1rbYOzi3Lwn333YdPfOIT2LVrF/bv349rr70W99xzz6L33n///bjk\nkkvw9re/Haeddhre/va347WvfS0eeOCBJrScmi2VUJHQKksuJDQZqYTapBYRtSb2FSJqFp5/qNU9\n9N8vAQAGehI4b2cfXrE1g71nDwAAHvzPY4iiqJnNozbU1gXhjhw5giAIsHfv3tJr+/btwx133LHo\nvVdddRU8z1v0ej6fb2gbqTWJooDh/iQrwBItg32FiJqF5x9qZY4X4ImfjwEALn/19tJx+foLT8MT\nPx/H+LSF46dyOGO4q5nNpDbT1sH5+Pg4uru7IcvzX6Ovrw+O42B6eho9PT2l13fu3Fnx2V/84hf4\n8Y9/jLe97W0b1l5qLaIooCvJSppEy2FfIaJm4fmHWtVzL2XhB/HI+KvPGSi9fsE5gxAEIIqAxw6P\nMjinVWn7tHZVrUxtKv7ddWtX8pyamsIHP/hB7Nu3D294wxsa2kYiIiIiIuoszx6bBgCIAnDW9u7S\n65mUhrNPjwcIHzsy2pS2Uftq65FzTdMWBeHFvycSiaqfmZiYwHve8x4IgoDbbrttTft1HAemaa7p\ns9VYllXxX26X22237RqGUddt1ku9+2q5Rv08O20fnfAdOmUfrdxXgfbur51wfHTKPjrhOxS33ar9\ntZF9dT024vdS7tAL4wCA7YMpIPRgmvPTZ3/pzF48e2wah16cwvjkDET4G9q21dron91qtXL76t1X\nhaiNKxU8/vjjeMc73oEnn3wSohgnAfznf/4nfv/3fx+PP/74ovePjo7ine98JyRJwl133YX+/v5V\n7c80TRw+zHULiRbat29fs5tQgX2VqLpW66sA+ytRLa3WX9lXK/2vb4wgZwXYd1YSv/Ganop/e3nS\nxf/+l3g++tsu78PZ26oPGlJnqGdfbeuR8927d0OWZTzxxBO44IILAACPPfYY9uzZs+i9lmXh2muv\nhaIouOuuu9Db27vm/Q4PD6O7u3v5N66QZVk4evQoduzYUXPEn9vldlt5u62q3n21XKN+np22j074\nDp2yj1buq0B799dOOD46ZR+d8B2K+2hVjeyr67ERv5eiyRkbOSte3/yiV52B3bu3Vfz72UGIux76\nVzheCDNKY8eO7RvWtrXYyJ/dWrRy++rdV9s6ONd1HVdccQVuvPFGfOpTn8Lo6CgOHjyIW265BUCc\nwp5Op6FpGr70pS/hxIkTuOuuuxCGISYmJkrbSKVSq9qvpmkNSTVKJBLcLrfbttttRY3qq+U24ufZ\nCfvohO/QSftoRZ3QXzvl+OiEfXTCd2hVG9FX12Mjfi9PPJct/fn8Vw5V3d85Z/Tiyecm8NzLuVJA\n2erHDNvXfG1dEA4Arr/+euzZswfvete7cNNNN+FDH/oQ9u/fDwC49NJLS+uYP/jgg7BtG1dffTUu\nu+yy0v9vvvnmZjafiIiIiIjayPHRHABAkUVsHag+yLdrR5yl+/PjWfhBuGFto/bW1iPnQDzyfeDA\nARw4cGDRvx05cqT052KQTkREREREtFYvzQXn2wdTkObWN19o91xw7noBjp3KbVjbqL21/cg5ERER\nERHRRikG56cNpmu+55wz5ovEPXt8puFtos7A4JyIiIiIiGgFgjDCy2N5AMBpQ7WD87ShYttcyvvz\nJxic08owOCciIiIiIlqBsSkTrh/PIV9q5BwAztoeV7U/OsK0dloZBudEREREREQrUExpB4DTtiy9\n4tOZ2zMAgJcnCqWAnmgpDM6JiIiIiIhWoBici6KA4f6lg/Od2+LgPIqA0azX8LZR+2NwTkRERERE\ntALFZdSG+5JQ5KVDqTPngnMAGJlicE7La/ul1IhaXRhGyFsuHC+ApkhIJVSINZbdIGoVPG6JqNXw\nvEStYGSiACBeRm05KUPFYK+BsSkTp6YZnNPyGJwTNVAYRhiZKMBy/NJrOc3DcH+SNxTUsnjcElGr\n4XmJWsXoVBycD/UlV/T+M7dlMDZlYmTKbWSzqEMwrZ1aVhhGmC04GM+amC04CMOo2U1atbzlVtxI\nAIDl+MhbPEFT66p13M4WnLbvk0TUnlr5etoJ9yu0MrbrY2rWAQAM9Rkr+kxx3vnYjMdjg5bFkXNq\nSZ3yhNzxglW9TtQKqh2fESKcGMtBV5XSa+3YJ4moPbXq9bRT7ldoZUanzNKfVzpyfvqWeLm1IARO\nTZk4K7Wyz9HmxJFzakmt/IR8NTRFWtXrRK2g2vHpOD6CBU/827FPElF7atXraafcr9DKnJqbbw6s\nfOT89KH5tdBPjBWWeCcRg3NqUc16Ql7v1LRUQkVCq0xQSWgyUgl1XdslqrfyYz8Mo0U3vKIoIqEp\niz7X7FErItocyq+nYRShYLuw3ThNuJmpwq06ok+NcWpu5FwQgC29KwvOh/uSkKU4i+KlsXzD2kad\ngWnt1JKa8YS8Ealp8RqYSVaXpZYWYe6GQ5gPvnVVwkCPDtcPoSkSwjDCRNZe9Nlmj1oR0eZQvJ7m\nTAcvjxUgiSI0VcJE1kbB8puWRt6qI/rUGKcm45HvvkwCiryy37Ekidjan8Tx0TxOMDinZTA4p5aU\nSqjIaV5FoNzoEeelUtO6klrVz0QA8qaHgmvWDLxFUaj5eaJW4AUCbCeArs8H57YboCulYqA7HhkI\nwwgFy69rn+SySESdb2E/F6O1j3KLogBBEKAuCHyXu1Y3UjPuV6h5Tk3GI+crTWkvOm0wNRecM62d\nlsbgnFpSM0acV5uaFoYRZswIY9MWdD2+2WARGGpHXlD9Zrn82K93n2QRJaLOV62fI/KwniT0Vksj\nZ4bc5lIcOR/qXV1Rt+2D8ftPThQQBCEkiTOLqToG59SyNnrEebWpaabtw7S9itea+fSeaK0UqfpN\n5OJ55/Xrk2vJVCGi9lKtn9tOAC9Ye+DaimnkzJDbHMIwKlVrH+pf/cg5APhBhJMTBZy2Jb3MJ2iz\n4mObDsE1NtdvtcXbXL+1nt4TrZUiRdC1yhvbRqdlttro13rw/Ns5+Lusr1r9uVa2zkqw0Co1Szbv\nwPNDAMCWntUF59u3zI+0Hx/N1bVd1Fk4ct4BaqWHZgymVK3GalPT1BqFQFgEhtqNAGCo10AoyBuW\nltmKo19rwfT8zsHfZf3V6s+1snVWgmnk1CwTWav054FVBudbegzIEuAHwPGRWfzy+Vvr3TzqEAzO\nO0Ct9FClzS5UYRjBDQRMzdrwo+ZcbFeTmmboMgy9cmkpPr2ndiWKAlJGY9IyqxVO7JQiSkzP7xz8\nXdZftX6uaxJsKVq2mOpSmEZOzTA+XRacdydW9VlRFNDfpeDUtIdjHDmnJTA47wC10sZqpV23ojCM\ncGrKxMSMg3TOhe2JdRuxaFTQL4oCMoaAwZ4EBFnh03vqKPWqpL5U4cROGP3qpPT8zY6/y/qrNsqN\nQMaxY40pphqGxaA/fiCo61HbnVOodY3PjZwLAtCb0Vf9+YFMHJwfP8XgnGpjcN4BaqWN1Uq7bkV5\ny4XtVN4A1WPEopFBPxCnA6cMBYaxuvQmolZWz/Te5QontvvoV6ek5xN/l42ycJR7bMJuSDHV4nlr\nesbCTN7F2LQFL5Q4LYHqZjwbF4Pr7dIhr6Ha+mAmDrtOjufh+SEUmaW/aDEeFR2gVnEUQ2+fZy+N\nGrFYKugnouqWSu9drU4vnMjiVJ2Dv8uN0ahzQj3PW0TVFOec968ypb1oIBNPhQzCCCcn8nVrF3WW\n9oneqKZaxVFs21r+wy2iXiMWC1NxLdev+r5OCQyIGqGeD8saUThxYT8Xo+ZV1GZxqs7B3+XGWMs5\nYSXTbDgtgRqtOOd8tfPNiwYz83WKjp/K4Yyhrrq0izoLg/MO0e7FUVIJdd1LOVVLxQ2jEBEW37gz\nTZGotnqm99a7cGK1fo7Iq9LLN067n39pHn+Xjbfac8JKp9lwWgI12npHzrtTElRFhOuFnHdONTE4\np5YgigKGeg2MZzR0p1V0pROrHrGoltIGCBCEym0wTZFoafWspF7vwonV+rntBPACjm4StYPVnhNW\nWkW/eN6y7fn38XpP9eL5AaZzDgBgoGdtwbkoCNg2kMSLJ3N4iRXbqQYG59QyRFGAKkXo7dJhrGFJ\np2qpa6IgoMtQ0b+OoJ9os6l3em89CyfWSlH1gmaOnRPRaqzmnLDSdPXieUsRA0xNqBjsSaC/l8Xg\nqD4mZ+af+qw1rR0AtvXHwfnL45xzTtUxOKeWt9IlnWqlrumqtK6gn6iT1VpqsFXTe2v1c0XiDThR\nJ1ppunrxXsH1AyiSAEOXGZhT3VSucb72B81bB5IAgJfH8wjCCBKPUVqA1dqppRXnmo1NWZjJuRib\nsjAyUUAYLh4l64Sq9UQbqXypwewy/atVVOvnuiZBkVq3zUS0diupol9+r5DNuZiYcXBqymzpcxm1\nl+Ia58Da55wD8cg5AHh+iLEpc93tos7DqIVa2krnmgGdUbWeaCMttdRgK46aA9X7uRgpmBlvdsuI\nqBFWMs2mVi2KVj6XUXsprnGuyCIyqbXXMSiOnAPAibEchvuTS7ybNqO2Hzl3XRc33HADLrroIlx2\n2WU4ePDgsp957LHHsH///g1oHa3XapdGKabiDnQb6EpqNVPawjDCbMHBeNbEbMHh03XalNaz9FAz\n+9BK+zkRtT8uo0atYCIbzznv704sKjS8GsN9BoqH74kxzjunxdp+5PzWW2/FoUOHcPfdd+PEiRO4\n7rrrsG3bNrzxjW+s+v5nn30Wf/RHfwRN45PUdtCIpVFWuiwLUadba/9iHyKijcBl1KhVjE/HI+fr\nKQYHxCPvW/qSGJkoMDinqtp65NyyLNx33334xCc+gV27dmH//v249tprcc8991R9/9e+9jVcc801\n6O/v3+CWdrZGjqCtZK7Zai2VKk+0maQSKnSt8uZ1Jf2rXfoQM2SIWt9S/XSl55patSi4jBrVy3rX\nOC+3fTAFAFxOjapq65HzI0eOIAgC7N27t/Tavn37cMcdd1R9/yOPPIJPf/rTyOVyuP322zeqmR2t\n0SNo9VjSaWE1astduBZ6jOlvtNmIooChXgPjq1xqcKNSSFe6UkOtz3J0n6i1LddPLdeH7Xjw/BCK\nLELTZAgQai6jlrdczOZC9Gc0DPUa7OtUN8WCcGtd47zc9sE0/uvQKEfOqaq2Ds7Hx8fR3d0NWZ7/\nGn19fXAcB9PT0+jp6al4fzEg/8Y3vrGh7exkqynYtlbrWdKpvBp1OufC9kSEUQhBECCg8qLN9Dfa\njERRWPVSgxuRQrre4Hojzk1EtD5L9dNUQsVMzsF4dn59aUOX0ZvRq55rivcKshBgVIoYmFPdFCwP\nph0fp+tNawfmR85zpouZvINMitckmtf2ae2qWpmyVPy767ZWemWnavUiLNWqUQMChAXZretJlWfq\nLG02y003Ke8TedPDWnrEelPnW/3cRERL99O4rwvQ1PlA3LR9CBFWfb3mdZrWYyJbnzXOi4rBOcC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aafDfcnMWvaSGRlKJKAfkWH70dw/QCqIkGXA2TSGiDIkCQRsiJAkoBgrl4GED9YF2Weo6g2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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116370278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(trans_data, diag_kind='kde', plot_kws={'alpha': 0.2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里要注意一下plot_kws关键字。这个让我们能导入设置选项，用来控制非对角线上的绘图。查看seaborn.pairplot的字符串文档查看更多的设定选项。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Facet Grids and Categorical Data（多面网格和类别数据）\n",
    "\n",
    "如果遇到一些数据集，需要额外分组的维度，该怎么办？一个方法是使用类别变量来把数据可视化，利用facet grid（多面网格）。seaborn有一个有用的内建函数factorplot，能简化制作各种多面图的过程："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x116b28748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACYGEfaAADAKdXU1Cg/P/+0lt2/f79H2/bt23X48OHTWl9sbKysVm7GAODM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177604a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='day', y='tip_pct', hue='time', col='smoker', \n",
    "               kind='bar', data=tips[tips.tip_pct < 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在一个facet（面）内，不是通过time和不同的柱状颜色来分组，我们也能通过添加给每一个time值添加一行的方式来扩展多面网格："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.063204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.172069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.063204\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.191244\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.199886\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.162494\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.172069"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips[tips.tip_pct < 1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x118d4fe48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9/ZWTk6OePXtKkrKzsxUREeHW1+FwaOzYsWrSpIlee+01tW7d2mV9VFSUvvrq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\n1mvvR59GfUA/qV5N+3TVNuDKW33a9PDQrl07nTx5UpWVlWrU6OxVVDa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KlcrNzdXy5csv\nR+l1woWOb+PGjTVt2jQ5HA6tXr1a7733ntq3b68nn3xSkpSZman27dtrxowZmjFjhrk74uM2btyo\nHj16aO3atRoyZIimT5+uoqKi884/92f4m2++0dtvv62xY8fWdqn1wjvvvKNnn31WmZmZatKkSZ08\nDV6X0KdrF3368qBPX171qU8THnzMAw88oIULFyosLEx/+9vfNHnyZPXr10/vvvuu4W1MnjxZ0dHR\n6tmzp373u98pNze3FiuuWy52fBMSEjRz5kxFRESoa9euGjVqlPbt2ydJatGihRo1auRyOhjVi4mJ\n0fjx49WxY0c98MADKi8v14EDBwy//q677lKnTp10xRVX1GKVdUt6erp69+7t/NOnTx+VlpZKkgYO\nHKhevXqpe/fuJlfZMNCnaxd9+vKgT3tfQ+nT3PPgg4YNG6Zhw4bp1KlT+vzzz5Wdna0ZM2bommuu\nuehrLRaLrrzySudy06ZNdebMmdost8453/Ht1q2b/vSnP2ndunX65ptvdODAAe3cuVOVlZVml1zn\n/PrDpOoDvLy83PDr6+LTJ2rblClTlJCQ4DJmtVolSeHh4WaU1KDRp2sXfbr20ae9r6H0ac48+JBv\nv/1W8+fPdy63aNFCQ4cOVXZ2ttq3b6+tW7e6neaqqKhw246/v7/LMjePnXWh49uuXTv9+9//1t13\n363XXntN4eHhSklJ0YIFC0ysuO5q1Mi9tVT9HF7sZ9hisSgwMLD2iqujWrVqpU6dOrn8qcLNj5cP\nfbp20acvH/q09zWUPk148CEVFRXKyspSXl6ey7i/v78CAwMVGhoqf39/FRcXO9d9//33l7vMOutC\nx9dqtSooKEjbtm3Ta6+9pgkTJqh///4qLCx0mVuXr1H0FfwMoy6jT9cu+rRv4GcYF0J48CHdu3fX\ngAED9MADD2jt2rU6dOiQtm/frvT0dJWXl2vw4MHq0aOH1qxZo7179+qLL75QVlaWyzb47dX5Xez4\nxsfHq1GjRlq7dq0OHz6sjz76SJmZmZJ+OZUbHBysAwcO6NSpU2buSp3Gz3Dt4xjWHvp07aJP+wZ+\nhmtfXT6GhAcfs2jRIg0fPlyZmZm6/fbbdd9996m4uFirVq1ScHCwHnroITVr1kwjRoxQRkaGHnro\nIZfX8xuXC7vQ8W3Xrp2efPJJvfLKKxo2bJiWLVumJ554Qo0bN9bu3bslSaNGjdLKlSv1xBNPmLwn\ndcuvfy75GfbchY5Jdes4hrWLPl276NPmoE/XTEPq03zDNAAAAABDOPMAAAAAwBDCAwAAAABDCA8A\nAAAADCE8AAAAADCE8AAAAADAEMIDAAAAAEMIDwAAAAAMITwAAAAAMITwAAAAAMAQwgNQB0RGRuq9\n994zuwwAwHnQp9FQEB4AAAAAGEJ4AAAAAGAI4QHwMYWFhfrLX/6iPn36aMCAAVq7dq1zncPh0JIl\nS3TbbbepR48eio2N1b333qsffvhBkpSRkaGEhASX7RUVFalXr17avHnzZd0PAKiv6NNoyAgPgA+p\nqKjQPffco1OnTumNN97QokWL9Oqrr8pisUiSXn/9dS1fvlxpaWnasGGDXnzxRRUUFGj+/PmSpDvu\nuEMHDx7U119/7dzmunXr1KJFC918882m7BMA1Cf0aTR0fmYXAOAX//73v7V//359/PHH6tixo6Sz\nv6VKSkqSJEVERGjBggXq37+/JCksLEy33Xab/vGPf0iSunXrpu7du+v9999Xnz59JEnvvfeeEhMT\nnR9sAIBLR59GQ8eZB8CH7N27V82bN3d+IElnn+BhtVolSQMGDFDLli31wgsv6OGHH1ZSUpKysrJU\nUVHhnD9ixAh99NFHOn36tL777jt98803uuOOOy77vgBAfUSfRkNHeAB8iMVikcPhcBv38zt7knDp\n0qUaO3asTp48qZtuuklPPfWU7r77bpe5v/vd71RWVqZPP/1Uf//739WrVy917tz5stQPAPUdfRoN\nHZctAT4kMjJSP//8s/bv36+uXbtKkgoKClRUVCRJWrJkiSZOnKiUlBTna5YtW+byQdasWTPFx8dr\nw4YNysvL05///OfLuxMAUI/Rp9HQceYB8CE33HCDevbsqWnTpmn79u3Kzc1VamqqGjduLEnq0KGD\n/ud//kf79+9Xfn6+nnvuOX388ccqLy932c6IESP08ccf64cfftDQoUPN2BUAqJfo02joCA+AD7FY\nLFq6dKm6dOmie+65R3/5y180bNgwtWzZUpK0YMEC2e12/f73v9eYMWO0b98+PfXUUzp+/LiOHj3q\n3M6NN96oli1bKj4+Xk2bNjVrdwCg3qFPo6GzOKq7cA9AnVZcXKx+/frpxRdf1A033GB2OQCAc9Cn\nUVdxzwNQj/z000/asmWL1q9fr/DwcD6QAMDH0KdR1xEegHrkzJkzmjlzpkJDQ/X888+bXQ4A4Bz0\nadR1XLYEAAAAwBBumAYAAABgCOEBAAAAgCGEBwAAAACGEB4AAAAAGEJ4AAAAAGAI4QEAAACAIYQH\nAAAAAIYQHgAAAAAYQngAAAAAYMj/D0lrcpZ9U5iEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118dba898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='day', y='tip_pct', row='time',\n",
    "               col='smoker', kind='bar', data=tips[tips.tip_pct])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "factorplot支持其他一些绘图类型，是否有用取决于我们想要如何展示。例如，box plots（箱线图，可以展示中位数，分位数和利群店）可能是一种有效的视觉类型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1151a8eb8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YWBiOOhGD0tLSVFBQYHcZkqSGhgY1NzerT58+3CgF+E7IvwLavXu3nnrqKd144416/PHH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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1191a0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='tip_pct', y='day', kind='box',\n",
    "               data=tips[tips.tip_pct < 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过seaborn.FacetGrid，我们可以创建自己的多面网格图。更多信息请查看seaborn的文档。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [py35]",
   "language": "python",
   "name": "Python [py35]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
